README.md

---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
---

# Qwen3-4B-Instruct-2507
<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;"/>
</a>

## Highlights

We introduce the updated version of the **Qwen3-4B non-thinking mode**, named **Qwen3-4B-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**.

![image/jpeg](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3-2507/Qwen3-4B-Instruct.001.jpeg)

## Model Overview

**Qwen3-4B-Instruct-2507** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 4.0B
- Number of Paramaters (Non-Embedding): 3.6B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- 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

|  | GPT-4.1-nano-2025-04-14 | Qwen3-30B-A3B Non-Thinking | Qwen3-4B Non-Thinking | Qwen3-4B-Instruct-2507 |
|--- | --- | --- | --- | --- |
| **Knowledge** | | | |
| MMLU-Pro | 62.8 | 69.1 | 58.0 | **69.6** |
| MMLU-Redux | 80.2 | 84.1 | 77.3 | **84.2** |
| GPQA | 50.3 | 54.8 | 41.7 | **62.0** |
| SuperGPQA | 32.2 | 42.2 | 32.0 | **42.8** |
| **Reasoning** | | | |
| AIME25 | 22.7 | 21.6 | 19.1 | **47.4** |
| HMMT25 | 9.7 | 12.0 | 12.1 | **31.0** |
| ZebraLogic | 14.8 | 33.2 | 35.2 | **80.2** |
| LiveBench 20241125 | 41.5 | 59.4 | 48.4 | **63.0** |
| **Coding** | | | |
| LiveCodeBench v6 (25.02-25.05) | 31.5 | 29.0 | 26.4 | **35.1** |
| MultiPL-E | 76.3 | 74.6 | 66.6 | **76.8** |
| Aider-Polyglot |  9.8 | **24.4** | 13.8 | 12.9 |
| **Alignment** | | | |
| IFEval | 74.5 | **83.7** | 81.2 | 83.4 |
| Arena-Hard v2* | 15.9 | 24.8 | 9.5 | **43.4** |
| Creative Writing v3 | 72.7 | 68.1 | 53.6 | **83.5** |
| WritingBench | 66.9 | 72.2 | 68.5 | **83.4** |
| **Agent** | | | |
| BFCL-v3 | 53.0 | 58.6 | 57.6 | **61.9** |
| TAU1-Retail | 23.5 | 38.3 | 24.3 | **48.7** |
| TAU1-Airline | 14.0 | 18.0 | 16.0 | **32.0** |
| TAU2-Retail | - | 31.6 | 28.1 | **40.4** |
| TAU2-Airline | - | 18.0 | 12.0 | **24.0** |
| TAU2-Telecom | - | **18.4** | 17.5 | 13.2 |
| **Multilingualism** | | | |
| MultiIF | 60.7 | **70.8** | 61.3 | 69.0 |
| MMLU-ProX | 56.2 | **65.1** | 49.6 | 61.6 |
| INCLUDE | 58.6 | **67.8** | 53.8 | 60.1 |
| PolyMATH | 15.6 | 23.3 | 16.6 | **31.1** |

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


## Quickstart

The code of Qwen3 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'
```

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/Qwen3-4B-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:
    ```shell
    python -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --context-length 262144
    ```
- vLLM:
    ```shell
    vllm serve Qwen/Qwen3-4B-Instruct-2507 --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](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': 'Qwen3-4B-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:

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}, 
}
```