This model is a int4 weight-only quantization with group_size 128 and symmetric quantization of Qwen/Qwen3.5-35B-A3B generated by AutoRound. Please follow the license of the original model.

Quantization Details

Attribute	Value
Base Model	Qwen/Qwen3.5-35B-A3B
Quantization Tool	AutoRound
Quantization Scheme	W4A16
Quantized Size	19924 MB

Evaluation Results

Task	Accuracy
hellaswag	0.6154
mmlu	0.8190
mmlu_abstract_algebra	0.6300
mmlu_anatomy	0.8296
mmlu_astronomy	0.9539
mmlu_business_ethics	0.8500
mmlu_clinical_knowledge	0.8906
mmlu_college_biology	0.9306
mmlu_college_chemistry	0.5900
mmlu_college_computer_science	0.6900
mmlu_college_mathematics	0.6900
mmlu_college_medicine	0.8555
mmlu_college_physics	0.6863
mmlu_computer_security	0.8400
mmlu_conceptual_physics	0.9362
mmlu_econometrics	0.7632
mmlu_electrical_engineering	0.8276
mmlu_elementary_mathematics	0.7989
mmlu_formal_logic	0.6825
mmlu_global_facts	0.5200
mmlu_high_school_biology	0.9484
mmlu_high_school_chemistry	0.7734
mmlu_high_school_computer_science	0.8900
mmlu_high_school_european_history	0.8848
mmlu_high_school_geography	0.9293
mmlu_high_school_government_and_politics	0.9741
mmlu_high_school_macroeconomics	0.8846
mmlu_high_school_mathematics	0.5741
mmlu_high_school_microeconomics	0.9454
mmlu_high_school_physics	0.7682
mmlu_high_school_psychology	0.9560
mmlu_high_school_statistics	0.8056
mmlu_high_school_us_history	0.9020
mmlu_high_school_world_history	0.9325
mmlu_human_aging	0.8206
mmlu_human_sexuality	0.9008
mmlu_humanities	0.7539
mmlu_international_law	0.9174
mmlu_jurisprudence	0.8611
mmlu_logical_fallacies	0.8773
mmlu_machine_learning	0.7679
mmlu_management	0.9320
mmlu_marketing	0.9402
mmlu_medical_genetics	0.9500
mmlu_miscellaneous	0.9349
mmlu_moral_disputes	0.8208
mmlu_moral_scenarios	0.5933
mmlu_nutrition	0.8725
mmlu_other	0.8584
mmlu_philosophy	0.8617
mmlu_prehistory	0.8858
mmlu_professional_accounting	0.7340
mmlu_professional_law	0.6773
mmlu_professional_medicine	0.9228
mmlu_professional_psychology	0.8856
mmlu_public_relations	0.7182
mmlu_security_studies	0.8367
mmlu_social_sciences	0.8999
mmlu_sociology	0.9104
mmlu_stem	0.7986
mmlu_us_foreign_policy	0.9200
mmlu_virology	0.5723
mmlu_world_religions	0.9006
piqa	0.8161

How to Use

HF Usage

Step 1: Install AutoRound

pip install auto-round

Step 2: Load and run the quantized model

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen3.5-35B-A3B-AutoRound-W4A16-ModelFree"

# 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 = "Write a quick sort algorithm."
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=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)

VLLM Usage

bash

vllm serve Qwen3.5-35B-A3B-AutoRound-W4A16-ModelFree \
    --trust-remote-code \
    --dtype bfloat16 \
    --tensor_parallel_size 1

If you encounter any issues, feel free to open an issue on the AutoRound GitHub repo or provide feedback on the Low-Bit Open LLM Leaderboard.

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:

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

This model is part of the Intel Low-Bit Open LLM Leaderboard initiative.

LeaderboardModel1/Qwen3.5-35B-A3B-AutoRound-W4A16-ModelFree

Qwen3.5-35B-A3B-AutoRound-W4A16-ModelFree

Model Details