Instructions to use Qwen/Qwen3-4B-Instruct-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-4B-Instruct-2507 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen3-4B-Instruct-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-Instruct-2507") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507", device_map="auto") 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-Instruct-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-Instruct-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-Instruct-2507", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen3-4B-Instruct-2507
- SGLang
How to use Qwen/Qwen3-4B-Instruct-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-Instruct-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-Instruct-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-Instruct-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-Instruct-2507", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/Qwen3-4B-Instruct-2507 with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3-4B-Instruct-2507
4b model with an 84.2 MMLU-Redux score?
A 4b dense model with an MMLU-Redux score on par with far larger top models is very hard to believe.
And after playing with this model for just a few minutes it's clear something's not right. It's only performing on par with ~65 MMLU-Redux scoring models.
Perhaps multiple choice testing is largely to blame since real-world use cases require the full and accurate retrieval of the desired information in response to prompts with highly variable wording and contexts, which is much harder than simply picking the correct answer out of a provided lineup.
But in many cases something else is going on. For example, an 80.2 ZebraLogic score is nonsense. There's nothing remotely special about the logical reasoning of this model. In fact, I can trip it up without even trying. Its score should be ~10-15. And the stated 54 English SimpleQA score of your Qwen3 235b model is equally insane. It can only answer comparably esoteric questions in the same domains covered by the test on par with ~15-17 scoring models.
Even if this was due to accidental test contamination why report what you know to be absurdly high test results in papers and model cards? It's like you're deliberately trying to make comparing LLM performance using standardized tests meaningless.
yeah,petty obvious,maybe just another examiner model or overfitting just for benchmark,it has improvements but not madness as the table show