The AWQ version is quantized using ms-swift. The AWQ models for Qwen3-235B-A22B-Instruct-2507 have been verified to work with both Transformers and vLLM.

Inference

use transformers:

python

from modelscope import AutoModelForCausalLM, AutoTokenizer

model_name = "swift/Qwen3-235B-A22B-Instruct-2507-AWQ"

# 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)

use vllm:

shell

VLLM_USE_MODELSCOPE=true vllm serve \
    swift/Qwen3-235B-A22B-Instruct-2507-AWQ \
    --tensor-parallel-size 4 \
    --max-model-len 262144

use ms-swift:

shell

# pip install git+https://github.com/modelscope/ms-swift.git
swift infer \
    --model swift/Qwen3-235B-A22B-Instruct-2507-AWQ \
    --infer_backend vllm \
    --vllm_tensor_parallel_size 4 \
    --vllm_max_model_len 262144

Quantization

The model has undergone AWQ int4 quantization using the ms-swift framework.

Quantization command:

shell

swift export \
    --model Qwen/Qwen3-235B-A22B-Instruct-2507 \
    --dataset 'swift/Chinese-Qwen3-235B-2507-Distill-data-110k-SFT' \
    --device_map auto \
    --quant_n_samples 256 \
    --quant_batch_size -1 \
    --max_length 12000 \
    --quant_method awq \
    --quant_bits 4 \
    --output_dir Qwen3-235B-A22B-Instruct-2507-AWQ

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.

Qwen3-235B-A22B-Instruct-2507

Highlights

We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements:

Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
Substantial gains in long-tail knowledge coverage across multiple languages.
Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
Enhanced capabilities in 256K long-context understanding.

Model Overview

Qwen3-235B-A22B-Instruct-2507 has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 235B in total and 22B activated
Number of Paramaters (Non-Embedding): 234B
Number of Layers: 94
Number of Attention Heads (GQA): 64 for Q and 4 for KV
Number of Experts: 128
Number of Activated Experts: 8
Context Length: 262,144 natively.

NOTE: This model supports only non-thinking mode and does not generate <think></think> blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Performance

	Deepseek-V3-0324	GPT-4o-0327	Claude Opus 4 Non-thinking	Kimi K2	Qwen3-235B-A22B Non-thinking	Qwen3-235B-A22B-Instruct-2507
Knowledge
MMLU-Pro	81.2	79.8	86.6	81.1	75.2	83.0
MMLU-Redux	90.4	91.3	94.2	92.7	89.2	93.1
GPQA	68.4	66.9	74.9	75.1	62.9	77.5
SuperGPQA	57.3	51.0	56.5	57.2	48.2	62.6
SimpleQA	27.2	40.3	22.8	31.0	12.2	54.3
CSimpleQA	71.1	60.2	68.0	74.5	60.8	84.3
Reasoning
AIME25	46.6	26.7	33.9	49.5	24.7	70.3
HMMT25	27.5	7.9	15.9	38.8	10.0	55.4
ARC-AGI	9.0	8.8	30.3	13.3	4.3	41.8
ZebraLogic	83.4	52.6	-	89.0	37.7	95.0
LiveBench 20241125	66.9	63.7	74.6	76.4	62.5	75.4
Coding
LiveCodeBench v6 (25.02-25.05)	45.2	35.8	44.6	48.9	32.9	51.8
MultiPL-E	82.2	82.7	88.5	85.7	79.3	87.9
Aider-Polyglot	55.1	45.3	70.7	59.0	59.6	57.3
Alignment
IFEval	82.3	83.9	87.4	89.8	83.2	88.7
Arena-Hard v2*	45.6	61.9	51.5	66.1	52.0	79.2
Creative Writing v3	81.6	84.9	83.8	88.1	80.4	87.5
WritingBench	74.5	75.5	79.2	86.2	77.0	85.2
Agent
BFCL-v3	64.7	66.5	60.1	65.2	68.0	70.9
TAU-Retail	49.6	60.3#	81.4	70.7	65.2	71.3
TAU-Airline	32.0	42.8#	59.6	53.5	32.0	44.0
Multilingualism
MultiIF	66.5	70.4	-	76.2	70.2	77.5
MMLU-ProX	75.8	76.2	-	74.5	73.2	79.4
INCLUDE	80.1	82.1	-	76.9	75.6	79.5
PolyMATH	32.2	25.5	30.0	44.8	27.0	50.2

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.

Quickstart

The code of Qwen3-MoE has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3_moe'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507"

# 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)

For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:

SGLang:
python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 --tp 8 --context-length 262144
vLLM:
vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507 --tensor-parallel-size 8 --max-model-len 262144

Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as 32,768.

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.

python

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-235B-A22B-Instruct-2507',

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Best Practices

To achieve optimal performance, we recommend the following settings:

Sampling Parameters:
- We suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
- For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
Adequate Output Length: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
- Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

chriswritescode/Qwen3-235B-A22B-Instruct-2507-AWQ-Swift

Qwen3-235B-A22B-Instruct-2507-AWQ

Intro

Inference

Quantization

Qwen3-235B-A22B-Instruct-2507

Highlights

Model Overview

Performance

Quickstart

Agentic Use

Best Practices

Citation

chriswritescode/
Qwen3-235B-A22B-Instruct-2507-AWQ-Swift