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Intel/
Qwen3.5-35B-A3B-int4-AutoRound

Name: Qwen3.5-35B-A3B-int4-AutoRound
Availability: InStock

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Model Details

This model is a int4 model with group_size 128 of Qwen/Qwen3.5-35B-A3B generated by intel/auto-round. Please follow the license of the original model.

vllm Infernece Example

bash

pip install git+https://github.com/vllm-project/vllm.git@main

pip install git+https://github.com/huggingface/transformers.git

vllm serve Intel/Qwen3.5-35B-A3B-int4-AutoRound  --port 8000   --tensor-parallel-size 1  --max-model-len 2048 --reasoning-parser qwen3 --served-model-name qwen --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

bash

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d ' {
    "model": "qwen",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Summarize Qwen 3.5 in one sentence."}
    ],
    "temperature": 1,
    "max_tokens": 512
  } '

Transformers Inference

Transformers v5.2.0

auto-round and This pr is required https://github.com/intel/auto-round/pull/1476

python

from transformers import AutoProcessor, Qwen3_5MoeForConditionalGeneration
model_name = "Intel/Qwen3.5-35B-A3B-int4-AutoRound"

model = Qwen3_5MoeForConditionalGeneration.from_pretrained(model_name, dtype="auto",
                                                                device_map="auto")
processor = AutoProcessor.from_pretrained(model_name)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image in short."},
        ],
    }
]


inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)


generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
print(processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])
"""
The user wants a short description of the image.

1.  **Identify the main subjects:** A woman and a large yellow dog (looks like a Labrador).
2.  **Identify the setting:** A sandy beach with the ocean in the background. The lighting suggests sunset or sunrise (golden hour).
3.  **Identify the action:** The dog is sitting and placing its paw in the woman's hand. The woman is smiling and sitting on the sand.
4.  **Synthesize into a short description:** A woman sitting on a beach at sunset gives a high-five (or "shake") to

"""

Generate the Model

This pr is required https://github.com/intel/auto-round/pull/1476

auto-round "Qwen/Qwen3.5-35B-A3B" --output_dir "./Qwen35-int4" --ignore_layers shared_expert,mtp.fc

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

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 [github](