Autoresearch/10k mar26#13
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Trained an 11.5M parameter GPT exclusively on SEC 10-K filings from financial companies. 23.3% better compression than the same model trained on general web text. ~20 experiments, ~2 hours total GPU time on a MacBook Air. Added: - prepare_10k.py: SEC EDGAR data pipeline (download, clean, shard) - benchmark.py: perplexity, inference speed, and cost benchmarks - monitor.py: live terminal dashboard with loss curves + thermal monitoring - MODEL_CARD.md: HuggingFace model card - Full README writeup of methodology, results, and learnings Results: - val_bpb: 1.645 (vs 2.146 general baseline) - Inference: 75,000+ tok/sec on Apple Silicon - Cost: $0 to process all 80K SEC filings (vs $15K+ via API) Model: https://huggingface.co/HarryS64/10k-financial-slm Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Built from ground truth: 1,131 filings = 136.7M tokens (our tokenizer), 120,910 tokens/filing, x0.875 GPT conversion = 105,796 GPT tokens/filing, x79,513 filings = 8.4B total tokens. All costs were ~40% underestimated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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