README.md

---
tags:
- unsloth
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-235B-A22B-Instruct-2507-FP8
---
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# Qwen3-235B-A22B-Instruct-2507-FP8
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
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## Highlights

We introduce the updated version of the **Qwen3-235B-A22B-FP8 non-thinking mode**, named **Qwen3-235B-A22B-Instruct-2507-FP8**, 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**.

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/62430a8522549d0917bfeb5a/Mul9SnL8LfUdREV3tB5W4.jpeg)

## Model Overview

This repo contains the FP8 version of **Qwen3-235B-A22B-Instruct-2507**, which 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](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).


## 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-FP8"

# 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:
    ```shell
    python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tp 4 --context-length 262144
    ```
- vLLM:
    ```shell
    vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 --tensor-parallel-size 4 --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.

## Note on FP8

For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`.

You can use the Qwen3-235B-A22B-Instruct-2507-FP8 model with serveral inference frameworks, including `transformers`, `sglang`, and `vllm`, as the original bfloat16 model.
However, please pay attention to the following known issues:
- `transformers`:
    - there are currently issues with the "fine-grained fp8" method in `transformers` for distributed inference. You may need to set the environment variable `CUDA_LAUNCH_BLOCKING=1` if multiple devices are used in inference.

## Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/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-FP8',

    # 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:

1. **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.

2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.

3. **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}, 
}
```