// Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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| name | unsloth |
| description | Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization |
| version | 1.0.0 |
| author | Orchestra Research |
| license | MIT |
| tags | ["Fine-Tuning","Unsloth","Fast Training","LoRA","QLoRA","Memory-Efficient","Optimization","Llama","Mistral","Gemma","Qwen"] |
| dependencies | ["unsloth","torch","transformers","trl","datasets","peft"] |
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