llm_lab/__init__.py

"""
LLM-1B-Lab: 1B Parameter LLaMA-style Transformer (from scratch)
================================================================
An educational implementation for deep learning beginners.
Each component includes detailed comments explaining "why" things are done this way.

Module structure:
  llm_lab.config      — All configurations (ModelConfig, DataConfig, TrainConfig, EvalConfig)
  llm_lab.model       — Model architecture (RMSNorm, RoPE, GQA, SwiGLU, Transformer)
  llm_lab.data        — Data pipeline (tokenizer, streaming, packing)
  llm_lab.training    — Training loop (Trainer, scheduler, checkpoint)
  llm_lab.evaluation  — Evaluation (Perplexity, generation, Scaling Law, Attention)
  llm_lab.utils       — Common utilities (device detection, seed)

Quick Start:
  from llm_lab.config import ModelConfig, DataConfig, TrainConfig
  from llm_lab.model import LLMModel
  from llm_lab.data import setup_data_pipeline
  from llm_lab.training import start_training
  from llm_lab.evaluation import run_evaluation
"""

__version__ = "0.1.0"

from .config import ModelConfig, DataConfig, TrainConfig, EvalConfig
from .model import LLMModel
from .data import setup_data_pipeline, setup_cpt_data_pipeline
from .training import start_training, start_cpt
from .evaluation import run_evaluation
from .utils import get_device, auto_configure