QwQ Bakeneko 32B GPTQ int8 (rinna/qwq-bakeneko-32b-gptq-int8)

Overview

This model is an 8-bit quantized model for rinna/qwq-bakeneko-32b using AutoGPTQ. The quantized version is 2x smaller than the original model and thus requires less memory and provides faster inference.

See rinna/qwq-bakeneko-32b for details about model architecture and data.

• Contributors

  • Toshiaki Wakatsuki
  • Xinqi Chen
  • Kei Sawada

• Release date

  March 13, 2025

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Benchmarking

For detailed benchmarking results, please refer to rinna's LM benchmark page (Sheet 20250313).

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How to use the model

Setup

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "rinna/qwq-bakeneko-32b-gptq-int8"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

messages = [
    {"role": "user", "content": "微分に関する簡単な文章問題を作成し、その問題を解いてください。"},
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
input_ids = tokenizer.encode(
    prompt,
    add_special_tokens=False,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=4096,
    do_sample=True,
    temperature=0.6,
    top_k=40,
    top_p=0.95,
)

response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
response = "<think>\n" + response
print(response)

Recommendations

For optimal performance, it is recommended to review Usage Guidelines before deploying this model.

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Tokenization

This model inherits the original Qwen/QwQ-32B tokenizer.

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How to cite

@misc{rinna/qwq-bakeneko-32b-gptq-int8,
    title = {rinna/qwq-bakeneko-32b-gptq-int8},
    author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
    url = {https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int8}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}

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References

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    title = {Qwen2.5 Technical Report}, 
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}

@misc{qwq32b,
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}

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}

@article{huang2023chat,
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@inproceedings{hong2024orpo,
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@article{frantar2022gptq,
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}

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License

The Apache License, Version 2.0