Skill: lnj22/mhc-layer-impl-nanogpt-training (version 0.1.0)

--- name: nanogpt-training description: Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop implementation. --- # NanoGPT Training ## Overview Training GPT-2 scale models (~124M parameters) efficiently on a single GPU. It provides: - **GPT-124M Architecture**: Standard transformer with RoPE and modern optimizations - **Tokenized Datasets**: Loading pre-tokenized shards from HuggingFace Hub or local files - **Modern Optimizers**: Muon optimizer with Newton-Schulz orthogonalization - **Mixed Precision**: bfloat16 training on A100 for 2x speedup Training options: - **Baseline GPT**: Standard residual connections - **Experimental residual variants**: Optional alternative residual schemes for stability/efficiency ## Quick Reference | Topic | Reference | |-------|-----------| | Model Architecture | [GPT Architecture](references/gpt-architecture.md) | | Data Loading | [Tokenized Data](references/tokenized-data.md) | | Optimizers | [Optimizers](references/optimizers.md) | | Training Loop | [Training Loop](references/training-loop.md) | | Hyperparameters | [Hyperparameters](references/hyperparameters.md) | ## Installation ```bash pip install torch einops numpy huggingface_hub ``` ## Minimal Example ```python import modal app = modal.App("gpt-training") image = modal.Image.debian_slim(python_version="3.11").pip_install( "torch", "einops", "numpy", "huggingface_hub" ) @app.function(gpu="A100", image=image, timeout=3600) def train(): import torch from dataclasses import dataclass @dataclass class GPTConfig: block_size: int = 1024 vocab_size: int = 50257 n_layer: int = 12 n_head: int = 12 n_embd: int = 768 dropout: float = 0.0 bias: bool = False # Download data, build model, train # ... (see references for full implementation) return {"final_loss": final_loss} @app.local_entrypoint() def main(): results = train.remote() print(results) ``` ## Common Imports ```python import torch import torch.nn as nn import torch.nn.functional as F from torch.cuda.amp import autocast, GradScaler from dataclasses import dataclass from einops import rearrange, repeat, reduce import numpy as np import math ``` ## When to Use What | Scenario | Approach | |----------|----------| | Standard GPT training | Use baseline model with standard residuals | | Stability experiments | Try alternative residual variants or extra streams | | Small experiments | Use T4/A10G GPU | | Full training | Use A100 with bfloat16 | | Custom data | Modify the dataset loader class | | Different model size | Adjust GPTConfig parameters | ## Metrics to Monitor | Metric | Typical Signal | Notes | |--------|----------------|-------| | Validation loss | Steady decrease | Absolute value depends on dataset/tokenizer | | Grad norm | Moderate, stable range | Large spikes indicate instability | | Training stability | Smooth curves | Frequent spikes suggest LR/batch issues | | Throughput | Consistent tokens/sec | Use for comparing configs | ## External Resources - nanoGPT: https://github.com/karpathy/nanoGPT - build-nanogpt: https://github.com/karpathy/build-nanogpt - modded-nanogpt: https://github.com/KellerJordan/modded-nanogpt - FineWeb-Edu token shards: https://huggingface.co/datasets/karpathy/fineweb-edu-100B-gpt2-token-shards


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