Unsloth
unslothai/unsloth
Faster, more memory-efficient fine-tuning / training helpers for LLMs. GitHub: unslothai/unsloth (~70k stars).
GitHub repository
Overview
Unsloth is the open-source project at unslothai/unsloth on GitHub (https://github.com/unslothai/unsloth). Faster, more memory-efficient fine-tuning / training helpers for LLMs. Category on LimeDock: ai-powered. Approximate community size: ~70k GitHub stars. Repo (copy/paste): https://github.com/unslothai/unsloth This LimeDock Directories page explains what it is and how teams use it in plain English — so searches for the GitHub project can land on a practical guide.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/unslothai/unsloth GitHub path: unslothai/unsloth
How to open it: 1. Copy this URL: https://github.com/unslothai/unsloth 2. Clone or follow the README install for your OS / stack. 3. Start with the smallest example in their docs before production use.
How to use it
**Simple example** Fine-tunes OOMs on your GPU and training is too slow.
1. Install Unsloth in a training env. 2. Follow a fine-tune notebook for a small model. 3. Eval before replacing your prompt-only stack.
Example prompts
- “Fine-tune a small model with Unsloth on our support Q&A.”
- “When is fine-tuning worth it vs better prompting/RAG?”
- “Estimate VRAM needs for this model.”
Use cases and examples
- Evaluate unslothai/unsloth for your stack
- Onboard a teammate to Unsloth with a shared checklist
- Compare Unsloth against tools you already pay for
- Capture lessons in your internal wiki after a pilot
Prerequisites
- GPU
- Clean dataset
- Eval set
Tips
- Garbage dataset → garbage model.
- Keep a prompt baseline for comparison.