import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer

model_name = "swift/Qwen3-30B-A3B-AWQ"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    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,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is 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)
print("content:", content)

Quantization

The model has undergone AWQ int4 quantization using the ms-swift framework. Since the model is based on the MoE (Mixture of Experts) architecture, all linear layers except for gate and lm_head have been quantized.

If you have fine-tuned the model and wish to quantize the fine-tuned version, you can refer to the following quantization scripts:

Dense Model Quantization Script: View Here
MoE Model Quantization Script: View Here

With these scripts, you can easily complete the quantization process for the model.

Evaluation

We evaluate the quality of this AWQ quantization with EvalScope.

Performance of Qwen3-30B-A3B-AWQ is evaluated on the mixed-benchmark of Qwen3 Evaluation Collection, with the results listed below:

The performance comparison of Qwen3-30B-A3B-AWQ and Qwen3-30B-A3B

task_type	dataset_name	metric	average_score(AWQ)	average_score(without AWQ)	count
exam	MMLU-Pro	AverageAccuracy	0.7655	0.7828	12032
exam	MMLU-Redux	AverageAccuracy	0.8746	0.8872	5700
exam	C-Eval	AverageAccuracy	0.844	0.8722	1346
instruction	IFEval	inst_level_strict_acc	0.8891	0.8925	541
instruction	IFEval	inst_level_loose_acc	0.9107	0.9174	541
instruction	IFEval	prompt_level_loose_acc	0.8651	0.8651	541
instruction	IFEval	prompt_level_strict_acc	0.8373	0.8318	541
math	MATH-500	AveragePass@1	0.944	0.938	500
knowledge	GPQA	AveragePass@1	0.596	0.601	198
code	LiveCodeBench	Pass@1	0.5275	0.5549	182
exam	iQuiz	AverageAccuracy	0.6917	0.7417	120
math	AIME 2024	AveragePass@1	0.7333	0.8333	30
math	AIME 2025	AveragePass@1	0.7	0.7333	30

NOTE: For the pass@k metric, considering time cost of evaluation, we uniformly limit the number of generated responses to 1

Conclusion

As we can see from the comparison above, evaluatoin results across different tasks and datasets suggest that our quantized-version with AWQ exihibit minimum fluctuation on model performance.

In fact, for most benchmarks, AWQ version performs mostly on-par with the original version, except for benchmarks (such as AIME2024 and iQuiz) where performance degration is relatively obvious.

surogate/Qwen3-30B-A3B-AWQ

Intro

Inference

Quantization

Evaluation

Conclusion

surogate/
Qwen3-30B-A3B-AWQ