# LLM Finetuning

> Fine-tunes open-source LLMs (Llama, Mistral, Qwen) using LoRA/QLoRA with HuggingFace and Unsloth. Use for domain-specific model adaptation.

- Skill: `ssrjkk/llm-finetuning` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add ssrjkk/llm-finetuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ssrjkk/llm-finetuning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ssrjkk (https://skillmd.com/u/ssrjkk)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/ssrjkk/llm-finetuning

---

# LLM Fine-Tuning

> Fine-tune open-source LLMs with LoRA/QLoRA for domain-specific tasks.

## Quick Start
```python
from unsloth import FastLanguageModel
from datasets import load_dataset
from trl import SFTTrainer

# Load model with LoRA
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Meta-Llama-3.1-8B",
    max_seq_length=2048,
    load_in_4bit=True,  # QLoRA
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_alpha=16,
    lora_dropout=0,
)

# Train
dataset = load_dataset("json", data_files="training_data.json")
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=2048,
)
trainer.train()
```

## When to Use
- Domain-specific model adaptation
- Task-specific fine-tuning (chat, code, classification)
- Not for simple prompt engineering tasks

## Step-by-Step Instructions
1. Choose base model (Llama 3, Mistral, Qwen)
2. Prepare training dataset in chat format
3. Configure LoRA/QLoRA parameters
4. Train and save adapter

## Dependencies
```bash
pip install unsloth transformers datasets trl accelerate
```

## Examples
Input: Training dataset of 1000 examples → Output: Fine-tuned LoRA adapter (50MB)

## Resources
- [Unsloth](https://github.com/unslothai/unsloth)
- [HuggingFace SFT](https://huggingface.co/docs/trl/sft_trainer)
- [Examples](./examples/)

## Troubleshooting
- **Loss plateaus early** — the learning rate is too high. Cut it by 10×
  and lower batch size so validation loss keeps dropping.
- **Catastrophic forgetting** — mix 5–10% of the original dataset into
  each epoch, or freeze the first third of the network with LoRA.
- **OOM during training** — use gradient accumulation, `gradient_checkpointing`
  (training-time), and 4-bit QLoRA quantization for consumer GPUs.
- **Model regurgitates training data** — you overfit. Raise dropout,
  shrink epochs, and add a validation split with early stopping.

## Validation
1. Training loss decreases consistently
2. Model generates coherent responses post-training
3. Adapter merges successfully (if needed)