Unnamed Skill

name: unsloth-core description: Core fundamentals of Unsloth for fast LLM fine-tuning, covering FastLanguageModel setup, optimized gradient checkpointing, and native inference acceleration (triggers: unsloth, FastLanguageModel, from_pretrained, get_peft_model, for_inference, gradient checkpointing).

Overview

Unsloth-core provides the foundational framework for accelerating Large Language Model (LLM) fine-tuning. It leverages specialized kernels and optimized gradient checkpointing to achieve up to 2x faster native inference and significant VRAM savings compared to standard Hugging Face implementations.

When to Use

• When fine-tuning Llama-3, Mistral, or Phi models and seeking maximum speed.
• When limited by VRAM and needing optimized gradient checkpointing.
• When deploying models for inference and requiring low-latency responses.

Decision Tree

1. Are you starting a new fine-tuning run?
   • Yes: Use FastLanguageModel.from_pretrained().
   • No (Inference only): Load with for_inference=True.
2. Is your VRAM usage too high?
   • Yes: Set use_gradient_checkpointing = 'unsloth'.
3. Are you ready for generation?
   • Yes: Call FastLanguageModel.for_inference(model) to enable 2x speed.

Workflows

1. Basic Model Initialization: Import FastLanguageModel, load a model with specific max_seq_length and load_in_4bit, and configure PEFT adapters via get_peft_model().
2. Optimized Training Configuration: Configure SFTTrainer with use_gradient_checkpointing = 'unsloth' and monitor loss values (ideally between 0.5 and 1.0).
3. Saving and Post-Training Inference: Save LoRA adapters using save_pretrained(), then enable optimized inference via for_inference(model) for generation.

Non-Obvious Insights

• Unsloth's native inference is not automatic; users must explicitly call FastLanguageModel.for_inference(model) to see the 2x performance gain.
• The specialized 'unsloth' gradient checkpointing option is 30% more VRAM efficient than the default standard implementation.
• Unsloth fixes known tokenizer and chat template bugs present in official model releases to improve fine-tuning accuracy.

Evidence

• "Unsloth itself provides 2x faster inference natively as well, so always do not forget to call FastLanguageModel.for_inference(model)." Source
• "We leverage our smart Unsloth gradient checkpointing algorithm... It smartly offloads intermediate activations to system RAM asynchronously whilst being only 1% slower." Source

Scripts

• scripts/unsloth-core_tool.py: Python utility for model initialization and training setup.
• scripts/unsloth-core_tool.js: Node.js interface for managing Unsloth training configurations.

Dependencies

• unsloth
• torch
• transformers
• peft
• trl

References

• references/README.md