torchtune

# Ensure you have a Hugging Face token (HF_TOKEN) set as an environment variable or pass it directly. # export HF_TOKEN="hf_YOUR_TOKEN" import os # 1. Download Llama2 7B model weights and tokenizer # You need access to Meta Llama models on Hugging Face. # This command will download files to /tmp/Llama-2-7b-hf print("Downloading model...") os.system( f"tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf " f"--ignore-patterns \"*.bin\" --hf-token {os.environ.get('HF_TOKEN', 'YOUR_HF_TOKEN_HERE')}" ) # 2. Copy a LoRA fine-tuning config for single device print("Copying config...") os.system("tune cp llama2/7B_lora_single_device ./my_llama2_lora_config.yaml") # (Optional) Modify my_llama2_lora_config.yaml if needed, e.g., adjust batch_size, # dataset path, or output_dir. Ensure tokenizer path points to the downloaded model. # Example: Update output_dir to a persistent location and ensure tokenizer path is correct. # 3. Run the LoRA fine-tuning recipe print("Running fine-tuning...") os.system( "tune run lora_finetune_single_device --config ./my_llama2_lora_config.yaml " "checkpointer.checkpoint_dir=/tmp/Llama-2-7b-hf " "tokenizer.path=/tmp/Llama-2-7b-hf/tokenizer.model " "output_dir=/tmp/torchtune_output" ) print("Fine-tuning command executed. Check /tmp/torchtune_output for results.")