Text Generation
Transformers
Safetensors
qwen3
conversational
Eval Results
text-generation-inference
Instructions to use Qwen/Qwen3-4B-Thinking-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-4B-Thinking-2507 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen3-4B-Thinking-2507") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Thinking-2507") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Thinking-2507") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen3-4B-Thinking-2507 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen3-4B-Thinking-2507" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3-4B-Thinking-2507", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen3-4B-Thinking-2507
- SGLang
How to use Qwen/Qwen3-4B-Thinking-2507 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Qwen/Qwen3-4B-Thinking-2507" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3-4B-Thinking-2507", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Qwen/Qwen3-4B-Thinking-2507" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen3-4B-Thinking-2507", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/Qwen3-4B-Thinking-2507 with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3-4B-Thinking-2507
Add community evaluation results for MMLU-PRO, GPQA
Browse filesThis PR adds community-provided evaluation results for the following benchmarks:
- **[MMLU-PRO](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro)**
**[GPQA](https://huggingface.co/datasets/Idavidrein/gpqa)**
These results were extracted from the model card. This is based on the new [evaluation results feature](https://huggingface.co/docs/hub/eval-results).*Note: This is an automated PR. Please review the evaluation results before merging.*
- .eval_results/gpqa.yaml +6 -0
- .eval_results/mmlu-pro.yaml +6 -0
.eval_results/gpqa.yaml
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- dataset:
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id: Idavidrein/gpqa
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value: 65.8
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source:
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url: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507
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name: Model Card
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.eval_results/mmlu-pro.yaml
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- dataset:
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id: TIGER-Lab/MMLU-Pro
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value: 74
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source:
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url: https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507
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name: Model Card
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