LLM Fine-tuning

🔧 LLM Fine-Tuning Process (Step-by-Step)

1. Pretrained LLM Selection

   • Choose a base model (e.g., GPT, BERT, LLaMA, Falcon, etc.)

   • These models are pretrained on large-scale datasets.
     
     

2. Task-Specific Data Preparation

   • Curate domain-specific or task-specific datasets (e.g., customer support chats, legal documents).

   • Format the data using prompt-response pairs or instruction-style inputs.
     
     

3. Tokenization

   • Convert raw text into tokens using the model's tokenizer (e.g., SentencePiece, Byte-Pair Encoding).
     
     

4. Fine-Tuning Loop

   • Input: Tokenized data

   • Model: Pretrained LLM

   • Loss: Cross-entropy loss (or task-specific objective)

   • Optimizer: AdamW (widely used)

   • Scheduler: Learning rate warm-up + decay
     
     

5. Validation & Evaluation

   • Use held-out datasets for measuring accuracy, BLEU, ROUGE, or other metrics.

   • Track overfitting or underfitting.
     
     

6. Model Saving and Deployment

   • Save the fine-tuned checkpoint.

   • Deploy using APIs, model servers (e.g., Triton, TorchServe), or embedding in applications.

🧰 Technologies Commonly Used

Stage Technology Stack

Model Framework PyTorch, TensorFlow, HuggingFace Transformers

Tokenization HuggingFace Tokenizers, SentencePiece

Training Infra GPUs (NVIDIA A100, V100), TPUs, Distributed Training (Deepspeed, FSDP, Horovod)

Optimization AdamW, LoRA (for lightweight fine-tuning), 8-bit quantization

Experiment Tracking Weights & Biases, MLflow

Deployment ONNX, TorchServe, Triton Inference Server, FastAPI