Create README.md

README.md

@@ -0,0 +1,188 @@
+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+---
+# 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}, 
+}
+```