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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Zero-friction LLM fine-tuning skill for Claude Code, Gemini CLI & any ACP agent. Unsloth on NVIDIA · TRL+MPS/MLX on Apple Silicon. Automates env setup, LoRA training (SFT, DPO, GRPO, vision), post-hoc GRPO log diagnostics, evaluation, and export end-to-end. Part of the Gaslamp AI platform.
| Date | Stars |
|---|---|
| 2026-07-24 | 266 |
| 2026-07-25 | 268 |
| 2026-07-28 | 268 |
| 2026-07-30 | 268 |
| 2026-07-31 | 269 |
| 2026-08-06 | 269 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.37%/day
# unsloth-buddy
<p align="center"><img src="images/unsloth_gaslamp.png" width="75%" alt="unsloth-buddy" /></p>
<p align="center">
<a href="https://github.com/TYH-labs/unsloth-buddy"><img src="https://img.shields.io/github/stars/TYH-labs/unsloth-buddy?style=flat&logo=github&color=181717&logoColor=white" alt="GitHub" /></a>
<a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="MIT License" /></a>
<a href="#quick-start"><img src="https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white" alt="Python 3.10+" /></a>
<a href="https://gaslamp.dev/unsloth"><img src="https://img.shields.io/badge/%F0%9F%94%A5%20Gaslamp-Compatible-ff6b00?logoColor=white" alt="Gaslamp Compatible" /></a>
<a href="#openclaw"><img src="https://img.shields.io/badge/%F0%9F%A6%9E%20OpenClaw-Compatible-ff4444" alt="OpenClaw Compatible" /></a>
<a href="#quick-start"><img src="https://img.shields.io/badge/%F0%9F%A4%96%20Agent-Claude%20Code%20%2F%20Codex%20%2F%20Gemini-8b5cf6" alt="Agent Compatible" /></a>
<a href="https://discord.gg/mZe4mbCQ6a"><img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white" alt="Discord" /></a>
<a href="#local-deploy"><img src="https://img.shields.io/badge/Backend-Unsloth%20%7C%20MLX%20%7C%20llama.cpp-00b4d8" alt="Backend: Unsloth / MLX / llama.cpp" /></a>
</p>
<p align="center"><code>/unsloth-buddy I have 500 customer support Q&As and want to fine-tune a summarization model. I only have a MacBook Air.</code></p>
<p align="center">
<a href="#quick-start"><img src="https://img.shields.io/badge/Try%20It-1%20minute-black?style=for-the-badge" alt="Try It" /></a>
<a href="demos/"><img src="https://img.shields.io/badge/Demos-Examples-6e40c9?style=for-the-badge" alt="Demos" /></a>
<a href="SKILL.md"><img src="https://img.shields.io/badge/10%2B%20Features-Details-0969da?style=for-the-badge" alt="Features" /></a>
</p>
<p align="center">
<a href="https://youtu.be/wG28uxDGjHE"><img src="https://img.shields.io/badge/▶%20Demo-YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white" alt="YouTube Demo" /></a>
<a href="https://www.bilibili.com/video/BV1VWAFzmECy/"><img src="https://img.shields.io/badge/▶%20演示-Bilibili-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white" alt="Bilibili Demo" /></a>
</p>
<p align="center">
English | <a href="README_zh-Hans.md">简体中文</a> | <a href="README_zh-Hant.md">繁體中文</a>
</p>
---
## What is this?
**The self-evolving fine-tuning agent.** It talks like a colleague, learns your setup's quirks over time, and orchestrates the full lifecycle: from data formatting and model selection to training, validation, and deployment.
Runs on NVIDIA GPUs via [Unsloth](https://github.com/unslothai/unsloth), natively on Apple Silicon via [mlx-tune](https://github.com/ml-explore/mlx-lm), and on free cloud GPUs via [colab-mcp](https://github.com/googlecolab/colab-mcp). Part of the [Gaslamp](https://gaslamp.dev/) AI development platform — [docs](https://gaslamp.dev/unsloth).
---
## One sentence, one fine-tuned model. One run, one step smarter.
```
You: Fine-tune a small model on my customer support FAQ. I have a CSV file.
[Phase 0] Creating project: customer_faq_sft_2026_03_17/
Injecting memory from past sessions...
Applied: adapter_path convention, SFT recipe for Apple Silicon, M4 profile
[Phase 1] Requirements interview...
Method: SFT Model: Qwen2.5-0.5B Deploy: Ollama
[Phase 2] Data strategy...
Loading 1,200 rows from faq.csv → reformatting as chat messages
Saved to data/train.jsonl (validated: messages column ✓)
[Phase 3] Environment: Apple M4 24GB, mlx-tune 0.4.3, Python 3.12
Ready for training
[Phase 4] Training... 200 steps
Final loss: 1.42 → saved to outputs/adapters/
[Phase 5] Evaluation (base vs fine-tuned, greedy decoding):
Q: How do I reset my password?
[Base] I can help with that. Which passExcerpt of 19,428 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:55750391437ec5a6, topic:fine-tuning, topic:lora, topic:qlora