Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

# Qwen3-4B-Instruct-2507

## 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 ```` 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}, } ```