How llama is put together
This is the (deprecated) Llama 2 reference repository from Meta: a minimal Python package for loading Llama 2 model checkpoints and running local text and chat inference. It is a small, single-package codebase (llama/) built directly on PyTorch with model-parallel support via fairscale and torchrun multi-process launching. Data flow: download.sh fetches signed-URL model weights/tokenizer to local directories; generation.py orchestrates tokenization, model forward passes across GPUs, and autoregressive decoding; example scripts are thin CLI entry points driven by the fire library. There is no server, API layer, or datastore — everything runs locally against checkpoint files on disk.
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Major components
llama.generation
Implements the Llama class that loads checkpoints, builds the Transformer and Tokenizer, and performs text-completion and chat-completion generation loops.
llama.model
Defines the Transformer architecture (attention, feed-forward, RMSNorm, rotary embeddings) with tensor/model parallelism via fairscale.
llama.tokenizer
Wraps sentencepiece to encode/decode text and manage BOS/EOS special tokens.
download.sh
Interactive shell script that downloads model weights and tokenizer from a user-supplied signed URL and verifies them with md5sum.
example_text_completion.py
CLI entry point demonstrating pretrained-model text completion via torchrun.
example_chat_completion.py
CLI entry point demonstrating fine-tuned chat-model dialogue completion using the chat template.
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