Nemotron 4 340B Instruct

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

import json
import requests

headers = {"Content-Type": "application/json"}

def text_generation(data, ip='localhost', port=None):
    resp = requests.put(f'http://{ip}:{port}/generate', data=json.dumps(data), headers=headers)
    return resp.json()


def get_generation(prompt, greedy, add_BOS, token_to_gen, min_tokens, temp, top_p, top_k, repetition, batch=False):
    data = {
        "sentences": [prompt] if not batch else prompt,
        "tokens_to_generate": int(token_to_gen),
        "temperature": temp,
        "add_BOS": add_BOS,
        "top_k": top_k,
        "top_p": top_p,
        "greedy": greedy,
        "all_probs": False,
        "repetition_penalty": repetition,
        "min_tokens_to_generate": int(min_tokens),
        "end_strings": ["<|endoftext|>", "<extra_id_1>", "\x11", "<extra_id_1>User"],
    }
    sentences = text_generation(data, port=1424)['sentences']
    return sentences[0] if not batch else sentences

PROMPT_TEMPLATE = """<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""

question = "Write a poem on NVIDIA in the style of Shakespeare"
prompt = PROMPT_TEMPLATE.format(prompt=question)
print(prompt)

response = get_generation(prompt, greedy=True, add_BOS=False, token_to_gen=1024, min_tokens=1, temp=1.0, top_p=1.0, top_k=0, repetition=1.0, batch=False)
response = response[len(prompt):]
if response.endswith("<extra_id_1>"):
    response = response[:-len("<extra_id_1>")]
print(response)

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"

#!/bin/bash
#SBATCH -A SLURM-ACCOUNT
#SBATCH -p SLURM-PARITION
#SBATCH -N 2
#SBATCH -J generation      
#SBATCH --ntasks-per-node=8   
#SBATCH --gpus-per-node=8
set -x

RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
OUTFILE="${RESULTS}/slurm-%j-%n.out"
ERRFILE="${RESULTS}/error-%j-%n.out"
MODEL=<PATH_TO>/Nemotron-4-340B-Instruct
CONTAINER="nvcr.io/nvidia/nemo:24.05"
MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"

read -r -d '' cmd <<EOF
bash /scripts/nemo_inference.sh /model
EOF

srun -o $OUTFILE -e $ERRFILE --container-image="$CONTAINER" $MOUNTS bash -c "${cmd}"