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Model: Irfanuruchi/Qwen3-4B-Computer-Science Source: Original Platform
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README.md
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README.md
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license: apache-2.0
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license_link: https://www.apache.org/licenses/LICENSE-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B
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datasets:
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- HuggingFaceTB/smoltalk
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- agentica-org/DeepCoder-Preview-Dataset
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- qwen3
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- computer-science
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- software-engineering
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- programming
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- python
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- code-generation
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- debugging
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- transformers
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- pytorch
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---
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# Qwen3-4B-Computer-Science
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Qwen3-4B-Computer-Science is a supervised fine-tuned language model based on **Qwen/Qwen3-4B**, designed for computer science and software engineering tasks.
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This repository contains the merged BF16 checkpoint compatible with the Hugging Face Transformers ecosystem.
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---
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# Model Summary
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The model specializes in programming-oriented instruction following across multiple computer science domains, including software engineering, debugging, algorithms, testing, and technical reasoning.
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Training was performed using parameter-efficient supervised fine-tuning (LoRA). The released checkpoint contains merged BF16 weights and can be used directly without PEFT adapters.
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---
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# Motivation
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General-purpose language models provide strong performance across many domains but are not specifically optimized for computer science workflows.
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Qwen3-4B-Computer-Science aims to improve programming-oriented instruction following while preserving the capabilities of the original Qwen3-4B base model.
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---
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# Model Details
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| Field | Value |
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|------|------|
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| Model Name | Qwen3-4B-Computer-Science |
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| Base Model | [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
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| Model Type | Causal Language Model |
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| Architecture | Decoder-only Transformer |
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| Parameters | 4 Billion |
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| Fine-Tuning | Supervised Fine-Tuning (SFT) |
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| Fine-Tuning Method | LoRA |
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| Training Strategy | Distributed Data Parallel (DDP) |
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| Released Weights | Merged BF16 |
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| Framework | Hugging Face Transformers |
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| Primary Language | English |
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---
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# Training
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Training was performed using supervised fine-tuning (SFT) with parameter-efficient fine-tuning (LoRA).
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Optimization utilized Distributed Data Parallel (DDP). After training, the LoRA adapters were merged into the base model to produce the released BF16 checkpoint.
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The published model does not require PEFT adapters during inference.
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---
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# Training Data
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The final training corpus contains **60,989** training examples and **512** evaluation examples.
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| Dataset | Configuration | License | Train | Eval |
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|---------|--------------|---------|------:|-----:|
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| HuggingFaceTB/smoltalk | smol-magpie-ultra | Apache-2.0 | 49,584 | 416 |
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| agentica-org/DeepCoder-Preview-Dataset | primeintellect | MIT | 11,405 | 96 |
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---
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# Dataset Attribution
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The model was fine-tuned using publicly available datasets released under their respective licenses.
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| Dataset | Configuration | License |
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|---------|--------------|---------|
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| HuggingFaceTB/smoltalk | smol-magpie-ultra | Apache-2.0 |
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| agentica-org/DeepCoder-Preview-Dataset | primeintellect | MIT |
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Credit for the datasets belongs to their respective authors.
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---
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# Intended Use
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Recommended applications include:
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- Software engineering
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- Programming assistance
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- Python development
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- Code generation
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- Code explanation
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- Debugging
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- Unit testing
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- Technical documentation
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- Computer science education
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---
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# Capabilities
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The model has been fine-tuned for:
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- Programming-oriented instruction following
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- Code generation
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- Code completion
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- Code explanation
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- Refactoring
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- Debugging
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- Algorithm implementation
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- Standard library usage
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- Technical reasoning
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The model inherits the general instruction-following capabilities of Qwen3-4B.
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---
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# Installation
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```bash
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pip install -U transformers accelerate torch
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```
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---
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# Usage
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```python
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from transformers import AutoTokenizer
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from transformers import AutoModelForCausalLM
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model_name = "Irfanuruchi/Qwen3-4B-Computer-Science"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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)
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```
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## Example
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```python
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messages = [
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{
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"role": "user",
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"content": "Implement binary search in Python."
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}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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# Hardware Requirements
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This repository contains merged BF16 weights.
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Memory requirements depend on the selected precision and inference backend.
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Users with limited GPU memory are encouraged to use the GGUF release when available.
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---
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# Limitations
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Although specialized for computer science tasks, the model remains a probabilistic language model.
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Outputs should be reviewed before use in production environments.
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The model may:
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- generate incorrect code
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- hallucinate APIs or libraries
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- produce incomplete implementations
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- misunderstand project-specific context
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---
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# License
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This repository is released under the **Apache License 2.0**.
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## Base Model
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This project is derived from **Qwen/Qwen3-4B**, which is distributed under the Apache License 2.0.
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## Training Data
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The datasets retain their original licenses.
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| Dataset | License |
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|---------|---------|
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| HuggingFaceTB/smoltalk | Apache-2.0 |
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| agentica-org/DeepCoder-Preview-Dataset | MIT |
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---
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# Acknowledgements
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This project builds upon the work of:
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- Alibaba Qwen Team
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- Hugging Face
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- HuggingFaceTB
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- Agentica
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- Unsloth
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The contributions of these open-source projects made this work possible.
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---
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# Citation
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```bibtex
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@misc{uruci2026qwen3cs,
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title={Qwen3-4B-Computer-Science},
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author={Irfan Uruçi},
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year={2026},
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publisher={Hugging Face},
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howpublished={https://huggingface.co/Irfanuruchi/Qwen3-4B-Computer-Science}
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}
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```
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---
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# Contact
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Questions, bug reports, and suggestions are welcome through the Hugging Face repository discussions.
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