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Skill v1.0.0
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PublishedJuly 29, 2026 at 12:07 PM
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version: "1.0.0" name: unsloth description: "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." tags: [qlora-finetuning, lora-finetuning, memory-efficient-tuning, quantized-llm-training, 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 FastLanguageModelimport torchmodel, 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]))