xgen-7b-8k-inst

Name: xgen-7b-8k-inst
Author: Salesforce

859

7.0B

llama

Salesforce

Language Model

OTHER

7B params

New

859 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

Official research release for the family of XGen models (`7B`) by Salesforce AI Research: Title: Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input...

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Code Examples

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

pythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)

header = (
    "A chat between a curious human and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = ""  # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"

inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))

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