Fine-tuning LLMs with Custom Data Using LoRA and QLoRA
Complete guide to fine-tuning language models with efficient parameter adaptation techniques.
Fine-tune a large language model for a specific domain, minimizing computational costs with PEFT (Parameter-Efficient Fine-Tuning) techniques.
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Prompt objective
Fine-tune a large language model for a specific domain, minimizing computational costs with PEFT (Parameter-Efficient Fine-Tuning) techniques.
Real use case
Minas Gerais legaltech JurisAI wants to fine-tune an LLM to answer questions about Brazilian labor legislation. They have 15,000 lawyer-reviewed question-answer pairs and a budget of R$ 5,000 (~$1,000 USD) for compute. They need a model that outperforms GPT-4 in this specific domain.
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Prompt
Create a complete fine-tuning pipeline for [BASE MODEL NAME: Llama 3/Mistral/Gemma] focused on [DOMAIN] using [LoRA/QLoRA].\\\\\\\\n\\\\\\\\n**Context:**\\\\\\\\n- Base model: [NAME AND SIZE: e.g. Llama 3.1 8B]\\\\\\\\n- Dataset: [NUMBER] examples of [TYPE: QA/instruction/chat/classification]\\\\\\\\n- Available hardware: [GPU: A100/L4/T4/RTX 4090] with [VRAM] GB\\\\\\\\n- Compute budget: R$ [AMOUNT]\\\\\\\\n- Goal: [DESCRIBE WHAT THE MODEL SHOULD DO BETTER]\\\\\\\\n\\\\\\\\n**1) Data Preparation:**\\\\\\\\n- Dataset format (Alpaca, ShareGPT, chat template)\\\\\\\\n- Cleaning: duplicate removal, normalization, quality validation\\\\\\\\n- Split: train (80%) / validation (10%) / test (10%)\\\\\\\\n- Tokenization and length distribution analysis\\\\\\\\n- Augmentation strategies (if small dataset < 5,000)\\\\\\\\n- Consistent prompt template with base model\\\\\\\\n- Example of 3 correctly formatted records\\\\\\\\n\\\\\\\\n**2) LoRA/QLoRA Configuration:**\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`python\\\\\\\\n# Recommended configuration\\\\\\\\n\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\\\\\\\\\\\\\\\\`\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\`r\\\\\\\\\\\\\\\\\\\\\\\` (rank): [4/8/16/32] — quality vs. memory trade-off\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\`lora_alpha\\\\\\\\\\\\\\\\\\\\\\\`: [16/32] — scaling factor\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\`target_modules\\\\\\\\\\\\\\\\\\\\\\\`: which layers to adapt (q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj)\\\\\\\\n- \\\\\\\\\\\\\\\\\\\\\\\`lora_dropout\\\\\\\\\\\\\\\\\\\\\\\`: [0.05/0.1]\\\\\\\\n- Quantization: 4-bit (QLoRA) with nf4 + double quantization\\\\\\\\n- Estimated trainable vs. total parameters\\\\\\\\n\\\\\\\\n**3) Training Loop:**\\\\\\\\n- Framework: Hugging Face TRL (SFTTrainer)\\\\\\\\n- Hyperparameters:\\\\\\\\n - Learning rate: [1e-4/2e-4/5e-5] with cosine scheduler\\\\\\\\n - Effective batch size: [VALUE] (gradient accumulation)\\\\\\\\n - Epochs: [1-5] (monitor for overfitting)\\\\\\\\n - Max sequence length: [512/1024/2048/4096]\\\\\\\\n - Warmup steps: [VALUE]\\\\\\\\n- Mixed precision: bf16 (if A100) or fp16 (if T4/RTX)\\\\\\\\n- Gradient checkpointing for memory savings\\\\\\\\n- WandB/MLflow for experiment tracking\\\\\\\\n\\\\\\\\n**4) Evaluation:**\\\\\\\\n- Automatic metrics: perplexity, BLEU, ROUGE, BERTScore\\\\\\\\n- Human evaluation: quality rubric (1-5) with 100 test set examples\\\\\\\\n- Comparison: base model vs. fine-tuned vs. GPT-4 (baseline)\\\\\\\\n- Hallucination and factuality assessment in domain\\\\\\\\n- Portuguese benchmark (if available for domain)\\\\\\\\n\\\\\\\\n**5) Merge and Deploy:**\\\\\\\\n- Merge LoRA weights into base model\\\\\\\\n- Quantization for inference: GGUF (llama.cpp) or GPTQ\\\\\\\\n- Deploy: vLLM/TGI for high-performance API\\\\\\\\n- Estimated inference cost per 1K tokens\\\\\\\\n\\\\\\\\n**6) Cost Estimate:**\\\\\\\\n- Training cost (GPU-hours × price)\\\\\\\\n- Inference cost (per 1K tokens)\\\\\\\\n- Comparison with using API (OpenAI/Anthropic) for same volume\\\\\\\\n\\\\\\\\nProvide the complete Python script for the pipeline, from data prep to deploy.
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How to use this prompt
- 1Replace the key placeholders first: BASE MODEL NAME: Llama 3/Mistral/Gemma, DOMAIN, LoRA/QLoRA, NAME AND SIZE: e.g. Llama 3.1 8B.
- 2Replace any bracketed placeholders like [this] with your own context.
- 3Add extra background information when you want more tailored results.
- 4Combine multiple prompts in one conversation when you need a richer output.
- 5Save your best-performing prompts so they are easy to reuse later.
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