Delete files src/train.py with huggingface_hub

src/train.py

@@ -1,172 +0,0 @@
-from pathlib import Path
-
-import torch
-
-from src.infra.tokenizer import CharacterTokenizer
-from src.infra.dataset import AutoRegressiveDataset, split_tokens
-
-from src.task.config import ModelConfig,TrainingConfig
-
-from src.task.model import TinyTransformerLM
-from src.task.training import train, evaluate
-
-
-
-def separator(title: str) -> None:
-    print("\n" + "=" * 60)
-    print(title)
-    print("=" * 60)
-
-
-# --------------------------------------------------
-# Configuration
-# --------------------------------------------------
-
-separator("Configuration")
-
-torch.manual_seed(42)
-
-model_config = ModelConfig()
-training_config = TrainingConfig()
-
-print(model_config)
-print(training_config)
-
-
-# --------------------------------------------------
-# Load corpus
-# --------------------------------------------------
-
-separator("Corpus")
-
-data_path = Path("data/tiny_shakespear.txt")
-
-text = data_path.read_text(
-    encoding="utf-8",
-)
-
-print(f"Corpus length: {len(text):,} characters")
-
-
-# --------------------------------------------------
-# Tokenization
-# --------------------------------------------------
-
-separator("Tokenization")
-
-tokenizer = CharacterTokenizer(text)
-
-tokens = torch.tensor(
-    tokenizer.encode(text),
-    dtype=torch.long,
-)
-
-print(f"Vocabulary size: {tokenizer.vocab_size}")
-print(f"Token count: {len(tokens):,}")
-
-
-# --------------------------------------------------
-# Train / validation / test split
-# --------------------------------------------------
-
-separator("Data Split")
-
-train_tokens, val_tokens, test_tokens = split_tokens(
-    tokens,
-)
-
-print(f"Train tokens: {len(train_tokens):,}")
-print(f"Val tokens:   {len(val_tokens):,}")
-print(f"Test tokens:  {len(test_tokens):,}")
-
-
-# --------------------------------------------------
-# Datasets
-# --------------------------------------------------
-
-separator("Datasets")
-
-train_dataset = AutoRegressiveDataset(
-    tokens=train_tokens,
-    context_length=model_config.context_length,
-)
-
-val_dataset = AutoRegressiveDataset(
-    tokens=val_tokens,
-    context_length=model_config.context_length,
-)
-
-test_dataset = AutoRegressiveDataset(
-    tokens=test_tokens,
-    context_length=model_config.context_length,
-)
-
-print(f"Train examples: {len(train_dataset):,}")
-print(f"Val examples:   {len(val_dataset):,}")
-print(f"Test examples:  {len(test_dataset):,}")
-
-
-# --------------------------------------------------
-# Model
-# --------------------------------------------------
-
-separator("Model")
-
-model = TinyTransformerLM(
-    config=model_config,
-    vocab_size=tokenizer.vocab_size,
-)
-
-print(model)
-
-
-# --------------------------------------------------
-# Training
-# --------------------------------------------------
-
-separator("Training")
-
-train(
-    model=model,
-    train_dataset=train_dataset,
-    val_dataset=val_dataset,
-    model_config=model_config,
-    training_config=training_config,
-)
-
-
-# --------------------------------------------------
-# Final test evaluation
-# --------------------------------------------------
-
-separator("Test Evaluation")
-
-test_loss = evaluate(
-    model=model,
-    dataset=test_dataset,
-    model_config=model_config,
-    training_config=training_config,
-)
-
-print(f"Test loss: {test_loss:.4f}")
-
-
-# --------------------------------------------------
-# Save checkpoint
-# --------------------------------------------------
-
-separator("Saving")
-
-checkpoint_dir = Path("checkpoints")
-checkpoint_dir.mkdir(parents=True, exist_ok=True)
-
-checkpoint_path = (
-    checkpoint_dir / "tiny-sentence-transformer.pt"
-)
-
-torch.save(
-    model.state_dict(),
-    checkpoint_path,
-)
-
-print(f"Checkpoint saved to: {checkpoint_path}")