⚡Unsloth
unslothai/unsloth · homepage ↗
Local UI to run and train LLMs and diffusion models, including Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, FLUX and more.
⭐ Hugely popular: 72k stars, gaining about 1k a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Reduces GPU memory usage, making large-scale model tuning accessible on consumer hardware.
- Supports 4-bit and 16-bit QLoRA fine-tuning, optimizing efficiency without compromising accuracy.
- Allows for exporting models to GGUF, Ollama, and Hugging Face with seamless integration.
under the hood
- Written in OpenAI's Triton, ensuring optimized backpropagation and fast training loops.
- Implements advanced tensor techniques for memory efficiency in fine-tuning tasks.
- Benchmarked against Hugging Face’s standard implementations, showing significant speed and context-length improvements.
our take from PR#2, 2025-02-19
star history
- PR#2 30k 2025-02-19
- now 72k + 42k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Quietly building: more output than attention, for now.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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Faster, Memory-Efficient Fine-Tuning for LLMs
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