qwen3-4b-custom-Sile

Qwen3-4B (Custom Fine-Tune) Sile

---

Model Summary

• Author: rfcoder0
• Model Type: Qwen3-4B base, custom fine-tuned Sile
• Hardware Used: Single RTX 3060 (12 GB) + RTX 3070 (8gb)
• Training: Proprietary fine-tune on a curated dataset
• Evaluation: lm-evaluation-harness, 5-shot

This fine-tuned Qwen3-4B demonstrates performance comparable to, and in some cases exceeding, 7B–8B parameter models on standard reasoning and commonsense benchmarks.

---

Benchmark Results (5-shot)

Values are mean ± stderr. Results produced locally with lm-eval-harness, batch_size=1.

---

Comparison (acc_norm)

---

Notes

• These results were obtained on a single consumer GPU (RTX 3060) and RTX 3070 (8gb).
• The fine-tune procedure and dataset remain proprietary.
• Scores indicate that with high-quality data and efficient training, a 4B parameter model can rival or outperform 7B–8B baselines on reasoning and commonsense benchmarks.

---

Usage

Weights are not provided. This repository serves as a benchmark disclosure.
If you wish to reproduce similar results, see lm-evaluation-harness for methodology.

---

License

This model is licensed under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0).

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit.
• NonCommercial — You may not use the material for commercial purposes.

Full license text: CC BY-NC 4.0 .

Support

If you find this work valuable and want to support further experiments:

• Bitcoin: bc1q76vw4krfx24gvz73pwmhav620xe6fxkxdh0s48
• Other: Feel free to contact me for additional options.

---

Citation

If you reference these results, please cite this repository:

@misc{rfcoder02025qwen4b,
  title  = {Qwen3-4B (Sile)},
  author = {Rob Hak},
  year   = {2025},
  url    = {https://huggingface.co/rfcoder0/qwen3-4b-custom-Sile}
}