license: apache-2.0
base_model:
- Qwen/Qwen3-30B-A3B-Thinking-2507
Model Details
This model is an int4 model with group_size 128 and symmetric quantization of Qwen/Qwen3-30B-A3B-Thinking-2507 generated by intel/auto-round algorithm. Please follow the license of the original model.
How To Use
vLLM usage
vllm serve Intel/Qwen3-30B-A3B-Thinking-2507-int4-AutoRound --tensor-parallel-size 4 --max-model-len 32768
INT4 Inference on CPU/Intel GPU/CUDA
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Intel/Qwen3-30B-A3B-Thinking-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=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)
"""
....will update later...
"""
Generate the model
Here is the sample command to reproduce the model
auto-round --model Qwen/Qwen3-30B-A3B-Thinking-2507 --output_dir "./tmp_autoround" --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
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} }