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unsloth-fft

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Performing full fine-tuning (FFT) in Unsloth with 100% exact weight updates and optimized gradient checkpointing. Triggers include fft, full fine-tuning, full_finetuning, exact fine-tuning, and weight updates.

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Overview

Full Fine-Tuning (FFT) in Unsloth allows for 100% exact weight updates, bypassing the low-rank approximations of LoRA. By utilizing Unsloth's optimized gradient checkpointing, FFT can fit significantly larger batch sizes while ensuring total model modification.

When to Use

  • When performing base model pre-training or continued pre-training on large datasets.
  • When model-wide behaviors need modification that adapters (LoRA) cannot fully capture.
  • When sufficient VRAM is available to handle full model gradients.

Decision Tree

  1. Do you need to modify 100% of the model weights?
    • Yes: Proceed with FFT.
    • No: Use [[unsloth-lora]].
  2. Is VRAM limited (e.g., < 24GB for a 7B model)?
    • Yes: Enable use_gradient_checkpointing = 'unsloth' and adamw_8bit.
    • No: Use standard BF16 and high batch sizes.

Workflows

Initializing Full Fine-tuning

  1. Load the model using FastLanguageModel.from_pretrained with load_in_4bit=False and load_in_8bit=False.
  2. Pass full_finetuning=True in the initialization call to unlock all weight updates.
  3. Apply the 'unsloth' gradient checkpointing via FastLanguageModel.get_peft_model(model, use_gradient_checkpointing='unsloth').

FFT Memory Management

  1. Set per_device_train_batch_size to 1 to accommodate full weight gradients.
  2. Increase gradient_accumulation_steps to simulate effective batch sizes of 16-32.
  3. Use the adamw_8bit optimizer to reduce memory consumption by optimizer states.

Non-Obvious Insights

  • Unsloth's gradient checkpointing implementation is critical for FFT, as it uses 30% less VRAM and allows for 2x larger batch sizes compared to standard Hugging Face implementations.
  • Full fine-tuning is the preferred method for base model pre-training where the objective is knowledge injection rather than task instruction.
  • FFT in Unsloth is strictly 100% exact; there is no numerical drift compared to standard training, only efficiency gains.

Evidence

  • "To enable full fine-tuning (FFT), set full_finetuning = True." Source
  • "use_gradient_checkpointing = 'unsloth' uses 30% less VRAM, fits 2x larger batch sizes!" Source

Scripts

  • scripts/unsloth-fft_tool.py: Script to initialize a full fine-tuning session with Unsloth.
  • scripts/unsloth-fft_tool.js: Node.js utility to generate FFT training parameters.

Dependencies

  • unsloth
  • torch
  • transformers

References

  • [[references/README.md]]