What is Unsloth — Fine-Tune LLMs 2x Faster with 80% Less Memory?

Fine-tune Llama, Mistral, Gemma, and Qwen models 2x faster using 80% less VRAM. Open-source with no accuracy loss. Train on a single GPU what used to need four.

Is Unsloth — Fine-Tune LLMs 2x Faster with 80% Less Memory free to use?

Yes. Unsloth — Fine-Tune LLMs 2x Faster with 80% Less Memory is freely available on TokRepo. Check the Source & Thanks section on the asset page for the specific open-source license.

How do I install Unsloth — Fine-Tune LLMs 2x Faster with 80% Less Memory?

Visit the asset page on TokRepo and click "Copy for agent" to get the installation instructions. Most assets can be installed with a single command.

Unsloth — Fine-Tune LLMs 2x Faster with 80% Less Memory

What is Unsloth?

Unsloth is an open-source LLM fine-tuning library that delivers 2x speed and 80% memory savings with no loss in accuracy. Fine-tune 70B models on a single GPU.

In one sentence: Open-source LLM fine-tuning library — 2x speed, 80% VRAM savings, zero accuracy loss, supports Llama/Mistral/Gemma/Qwen — 25k+ GitHub stars.

For: ML engineers with limited GPU resources who need to fine-tune open-source LLMs.

Core Features

1. 2x Training Speed

Custom CUDA kernels optimize attention and backpropagation.

2. 80% VRAM Savings

Llama 3 8B needs only 6GB VRAM (vs. 24GB).

3. Multi-Format Export

GGUF (Ollama), merged 16-bit, and Hugging Face Hub.

4. Broad Model Support

Llama, Mistral, Gemma, Qwen, and Phi families.

FAQ

Q: Does it affect model quality? A: No — it's mathematically equivalent; speedups come from kernel optimization.

Q: Can I use consumer GPUs? A: Yes — an RTX 3060 (12GB) can fine-tune 8B models.