unsloth-fft
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.
Profile →npx skills add majiayu000/claude-skill-registry --skill unsloth-fft --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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')`
- Overview
- When to Use
- Decision Tree
- Workflows
- Initializing Full Fine-tuning
- FFT Memory Management
- Non-Obvious Insights
- Evidence
- Scripts
- Dependencies
- References
What does the unsloth-fft skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill unsloth-fft --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.