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Irfanuruchi
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Model Details
Model Provider
Irfanuruchi
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
python
messages = [{"role": "user","content": "Implement binary search in Python."}]text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True,)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs,max_new_tokens=512,)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
This repository contains merged BF16 weights.
Memory requirements depend on the selected precision and inference backend.
Users with limited GPU memory are encouraged to use the GGUF release when available.
Although specialized for computer science tasks, the model remains a probabilistic language model.
Outputs should be reviewed before use in production environments.
The model may:
This repository is released under the Apache License 2.0.
This project is derived from Qwen/Qwen3-4B, which is distributed under the Apache License 2.0.
The datasets retain their original licenses.
| Dataset | License |
|---|---|
| HuggingFaceTB/smoltalk | Apache-2.0 |
| agentica-org/DeepCoder-Preview-Dataset | MIT |
This project builds upon the work of:
The contributions of these open-source projects made this work possible.
bibtex
@misc{uruci2026qwen3cs,title={Qwen3-4B-Computer-Science},author={Irfan Uruçi},year={2026},publisher={Hugging Face},howpublished={https://huggingface.co/Irfanuruchi/Qwen3-4B-Computer-Science}}
Questions, bug reports, and suggestions are welcome through the Hugging Face repository discussions.