# Unsloth

> Fast QLoRA/QLoRA fine-tuning with 2x faster training and 50% less memory. Supports Llama, Mistral, Gemma, Qwen, DeepSeek, Phi, Yi, Falcon. Flash Attention, 4-bit quantization. No quality loss.

- Skill: `mkurman/unsloth` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mkurman/unsloth`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mkurman/unsloth/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: mkurman (https://skillmd.com/u/mkurman)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mkurman/unsloth

---

## Overview

Unsloth provides 2x faster QLoRA training with 50% less memory via optimized kernels. Supports Llama, Mistral, Gemma, Qwen 2.5, DeepSeek, Phi,  Yi, and Falcon with Flash Attention.

## Installation

```bash
uv pip install unsloth
```

## QLoRA Fine-Tuning

```python
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
    max_seq_length=4096,
    dtype=torch.bfloat16,
    load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
    model, r=16, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_alpha=16, use_gradient_checkpointing="unsloth",
)
print(model.print_trainable_parameters())
```

## Inference

```python
FastLanguageModel.for_inference(model)
inputs = tokenizer(["Describe quantum computing."], return_tensors="pt").to("cuda")
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=256)[0]))
```

## References
- [Unsloth GitHub](https://github.com/unslothai/unsloth)
- [Unsloth docs](https://docs.unsloth.ai/)