# LLM Finetuning

> Best practices for dataset preparation and PEFT/LoRA fine-tuning.

- Skill: `j4flmao/llm-finetuning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add j4flmao/llm-finetuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/j4flmao/llm-finetuning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: j4flmao (https://skillmd.com/u/j4flmao)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/j4flmao/llm-finetuning

---


# LLM Fine-Tuning (PEFT/LoRA)

## Dataset Preparation
- Format data strictly as Instruction/Input/Output pairs.
- Clean and deduplicate to prevent overfitting.

## Fine-Tuning Pipeline
```mermaid
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    A[Raw Data] --> B[Formatting & Tokenization]
    B --> C[Base Model]
    C --> D{Apply LoRA Adapters}
    D --> E[Training Loop]
    E --> F[Merged Checkpoint]
```

## LoRA Configuration Snippet
```python
import torch
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model

def setup_lora_model(model_id):
    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
    lora_config = LoraConfig(
        r=16,
        lora_alpha=32,
        target_modules=["q_proj", "v_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM"
    )
    return get_peft_model(model, lora_config)
```