palmyra-mini-thinking-b-GGUF

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1 language

Q4

license:apache-2.0

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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔴 Low Quality (3.8/10)

Researched training datasets used by palmyra-mini-thinking-b-GGUF with quality assessment

Specialized For

general

science

multilingual

Training Datasets (2)

🔴 2.5/10

general

science

Key Strengths

•Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
•Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
•Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...

Considerations

•Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
•Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

🟡 5/10

science

multilingual

Key Strengths

•High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
•Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
•Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...

Considerations

•Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
•Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Use with transformerspythontransformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Writer/palmyra-mini-thinking-b"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
)

messages = [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],

input_ids = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)

gen_conf = {
    "max_new_tokens": 256,
    "eos_token_id": tokenizer.eos_token_id,
    "temperature": 0.3,
    "top_p": 0.9,
}

with torch.inference_mode():
    output_id = model.generate(input_ids, **gen_conf)

output_text = tokenizer.decode(output_id[0][input_ids.shape[1] :])

print(output_text)

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

Running with vLLMpython

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Writer/palmyra-mini-thinking-b",
    "messages": [
      {
        "role": "user",
        "content": "You have a 3-liter jug and a 5-liter jug. How can you measure exactly 4 liters of water?"
      }
    ],
    "max_tokens": 8000,
    "temperature": 0.2
  }'

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