What models can I fine-tune with this skill?

The skill supports a wide range of popular models including Llama 3.3, 3.2, and 3.1, Mistral v0.3, Phi 3.5, Gemma 2, and Qwen 2.5.

What export formats are supported after training?

You can export your fine-tuned models to several formats including GGUF (for local LLM runners), Ollama, vLLM, and standard Hugging Face safetensors.

Can I use Unsloth on a consumer-grade GPU?

Yes, Unsloth is specifically optimized for efficiency, allowing you to train 1B models on GPUs with as little as 8GB VRAM and 7B-8B models on 12GB-16GB VRAM.

Does this skill help with CUDA 'Out of Memory' errors?

Absolutely. It includes specific patterns for memory optimization, such as adjusting gradient accumulation steps, reducing sequence length, and utilizing 4-bit quantization to fit larger models on smaller hardware.

Unsloth LLM Fine-Tuning

Name: Unsloth LLM Fine-Tuning
Author: ScientiaCapital

byScientiaCapital

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数据科学与机器学习

Accelerates LLM fine-tuning by 2x while reducing memory consumption by 80% for models like Llama, Mistral, and Phi.

关于

This skill provides Claude with expert capabilities to implement and manage LLM fine-tuning using the Unsloth library. It enables users to perform memory-efficient 4-bit quantization, configure LoRA/QLoRA adapters, and manage the entire training lifecycle from dataset integration to final model export. Whether you are working on consumer-grade hardware or enterprise GPUs, this skill helps optimize hyperparameters to prevent CUDA out-of-memory errors and facilitates exporting models into production-ready formats like GGUF, Ollama, and vLLM.

主要功能

Automatic 4-bit quantization and gradient checkpointing
Seamless export to GGUF, Ollama, and Hugging Face formats
1 GitHub stars
2x faster training speeds with 80% VRAM reduction
Support for Llama 3.3, Mistral, Phi 3.5, and Gemma 2
Hardware-specific performance optimization profiles

使用场景

Creating custom GGUF models for local deployment in Ollama
Fine-tuning Llama 3 models on consumer GPUs like the RTX 3060
Optimizing training pipelines for low-latency domain adaptation

关于

主要功能

Automatic 4-bit quantization and gradient checkpointing
Seamless export to GGUF, Ollama, and Hugging Face formats
1 GitHub stars
2x faster training speeds with 80% VRAM reduction
Support for Llama 3.3, Mistral, Phi 3.5, and Gemma 2
Hardware-specific performance optimization profiles

使用场景

Creating custom GGUF models for local deployment in Ollama
Fine-tuning Llama 3 models on consumer GPUs like the RTX 3060
Optimizing training pipelines for low-latency domain adaptation