---
license: apache-2.0
datasets:
- NeelNanda/pile-10k
base_model:
- Qwen/Qwen3-235B-A22B-Instruct-2507
---


## Model Details

This model is an int4 model with group_size 128 and symmetric quantization of [[Qwen/Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507)](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. 

Please follow the license of the original model.

## How To Use


**vLLM usage**

~~~bash
vllm serve Intel/Qwen3-235B-A22B-Thinking-2507-int4-AutoRound --tensor-parallel-size 4   --max-model-len 32768
~~~


**INT4 Inference on CPU/Intel GPU/CUDA**

~~~python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Intel/Qwen3-235B-A22B-Instruct-2507-int4-AutoRound"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

"""
content: A large language model (LLM) is a type of artificial intelligence trained on vast amounts of text data to understand and generate human-like language.
 Using deep learning techniques, particularly transformer architectures, LLMs learn patterns, grammar, facts, and reasoning abilities from diverse sources like books, websites, and articles.
 They can perform a wide range of language tasks, such as answering questions, writing stories, coding, and translating languages. Examples include models like GPT, Llama, and PaLM.
 Their power comes from their size—often billions of parameters—and extensive training, enabling them to produce coherent and contextually relevant responses.

"""
~~~

### Generate the model

Here is the sample command to reproduce the model

```bash
auto-round --model Qwen/Qwen3-235B-A22B-Instruct-2507 --output_dir ./Qwen3-235B-A22B-Instruct-2507-int4 --enable_torch_compile  --nsamples 512 --fp_layers mlp.gate
```



## Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

## Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)

## Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

## Cite

@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }

[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)