implementing-llms-litgpt

# LitGPT - Clean LLM Implementations ## Quick start LitGPT provides 20+ pretrained LLM implementations with clean, readable code and production-ready training workflows. **Installation**: bash pip install 'litgpt[extra]' **Load and use any model**: python from litgpt import LLM # Load pretrained model llm = LLM.load("microsoft/phi-2") # Generate text result = llm.generate( "What is the capital of France?", max_new_tokens=50, temperature=0.7 ) print(result) **List available models**: bash litgpt download list ## Common workflows ### Workflow 1: Fine-tune on custom dataset Copy this checklist: Fine-Tuning Setup: - [ ] Step 1: Download pretrained model - [ ] Step 2: Prepare dataset - [ ] Step 3: Configure training - [ ] Step 4: Run fine-tuning **Step 1: Download pretrained model** bash # Download Llama 3 8B litgpt download meta-llama/Meta-Llama-3-8B # Download Phi-2 (smaller, faster) litgpt download microsoft/phi-2 # Download Gemma 2B litgpt download google/gemma-2b Models are saved to `checkpoints/` directory. **Step 2: Prepare dataset** LitGPT supports multiple formats: **Alpaca format** (instruction-response): [ { "instruction": "What is the capital of France?", "input": "", "output": "The capital of France is Paris." }, { "instruction": "Translate to Spanish: Hello, how are you?", "input": "", "output": "Hola, ¿cómo estás?" } ] Save as `data/my_dataset.json`. **Step 3: Configure training** bash # Full fine-tuning (requires 40GB+ GPU for 7B models) litgpt finetune meta-llama/Meta-Llama-3-8B --data JSON --data.json_path data/my_dataset.json --train.max_steps 1000 --train.learning_rate 2e-5 --train.micro_batch_size 1 --train.global_batch_size 16 # LoRA fine-tuning (efficient, 16GB GPU) litgpt finetune_lora microsoft/phi-2 --data JSON --data.json_path data/my_dataset.json --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 --train.max_steps 1000 --train.learning_rate 1e-4 **Step 4: Run fine-tuning** Training saves checkpoints to `out/finetune/` automatically. Monitor training: bash # View logs tail -f out/finetune/logs.txt # TensorBoard (if using --train.logger_name tensorboard) tensorboard --logdir out/finetune/lightning_logs ### Workflow 2: LoRA fine-tuning on single GPU Most memory-efficient option. LoRA Training: - [ ] Step 1: Choose base model - [ ] Step 2: Conf