LLM Fine-Tuning Infrastructure

LLM Fine-Tuning Infrastructure

Train and fine-tune open-source LLMs efficiently — from LoRA on a single GPU to distributed full fine-tuning across multi-node clusters.

When to Use This Skill

Use this skill when:

• Fine-tuning an LLM on domain-specific data (legal, medical, code, support)
• Running QLoRA to fine-tune 70B models on consumer GPUs
• Setting up distributed training with DeepSpeed or FSDP
• Exporting fine-tuned adapters for production serving
• Implementing RLHF, DPO, or instruction tuning pipelines

Prerequisites

• NVIDIA GPU(s) with 24GB+ VRAM (RTX 4090 / A100 / H100)
• CUDA 12.1+ and nvidia-smi working
• Python 3.10+ with pip
• Hugging Face account and HF_TOKEN for gated models
• 500GB+ disk for model weights and training data

Quick Start: QLoRA Fine-Tuning

pip install transformers datasets trl peft bitsandbytes accelerate

python - <<'EOF'
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer, SFTConfig
import torch

model_id = "meta-llama/Llama-3.1-8B-Instruct"

# 4-bit quantization (QLoRA)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,