🎯 Fine-tune কী?
Pre-trained model-কে আপনার specific task/data-এ further train। Better performance specific use case-এ।
🆚 RAG vs Fine-tune vs Prompt
| Approach | কখন |
|---|---|
| Better prompting | প্রথমে চেষ্টা — সবচেয়ে cheap |
| Few-shot | Pattern dorkar (10-50 example) |
| RAG | Up-to-date knowledge, citation |
| Fine-tune | Style/format consistency, latency, cost |
✅ কখন Fine-tune
- Specific output format consistent
- Domain language/jargon
- Tone/style match
- Smaller faster model দিয়ে বড় model match
- API cost reduce
- Privacy (own model)
❌ কখন Fine-tune করবেন না
- Knowledge add (RAG ভালো)
- Frequently changing facts
- Small dataset (<100 example)
- Just better quality (try GPT-4 first)
- One-time task
📚 Dataset Preparation
// JSONL format
{"messages": [
{"role": "system", "content": "You are a customer support."},
{"role": "user", "content": "How do I reset password?"},
{"role": "assistant", "content": "Click 'Forgot password'..."}
]}
{"messages": [...]}
// 100-1000+ high-quality examples
// Diverse, edge case included
🔬 OpenAI Fine-tune
// Upload file
const file = await openai.files.create({
file: fs.createReadStream("data.jsonl"),
purpose: "fine-tune"
});
// Create job
const job = await openai.fineTuning.jobs.create({
training_file: file.id,
model: "gpt-4o-mini-2024-07-18"
});
// Poll status, then use:
// model: "ft:gpt-4o-mini:org:custom-name:abc123"
🪶 LoRA (Low-Rank Adaptation)
Full fine-tune-এর চেয়ে ১০০x cheap। Small adapter weights train, base frozen।
- Memory efficient
- Multiple adapter swap
- Open source standard (PEFT library)
- QLoRA — 4-bit quantized + LoRA
🛠️ Open Source Fine-tune
# Hugging Face PEFT + LoRA
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3-8B")
config = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"])
model = get_peft_model(model, config)
# Train on your data with Trainer
📊 Evaluation
- Hold-out test set (separate from train)
- Same metric as task (accuracy, F1, BLEU)
- LLM-as-judge
- Human eval (sample)
- Compare baseline (no fine-tune)
- A/B test in production
⚠️ Pitfall
- Overfitting (small data)
- Catastrophic forgetting (general capability lost)
- Bias amplification
- Cost spiral (training compute)
- Stale (data outdated quickly)
- Hard to debug
💰 Cost
- OpenAI GPT-4o-mini: $3/1M training token
- OpenAI GPT-4o: $25/1M
- Inference: 2x base price
- Open source LoRA: free + GPU rental ($1-5/hour)
🎓 Alternative — Distillation
Big model (GPT-4) → generate training data → fine-tune small model। সস্তা, fast inference।
🔮 RFT (Reinforcement Fine-Tuning)
OpenAI 2025 — reward model দিয়ে fine-tune। Domain expert behavior শেখানো।
💡 Decision Tree ⭐
1. Better prompt try করুন 2. Few-shot example add 3. RAG (knowledge issue হলে) 4. Stronger model (GPT-4o → o1) 5. Still issue? → Fine-tune 6. Validate ROI বাজেটের against