| { |
| "repository_info": { |
| "name": "ericjm/narrow-data", |
| "description": "Experimental model checkpoints from 'On the creation of narrow AI' paper", |
| "version": "1.0", |
| "paper_title": "On the creation of narrow AI: hierarchy and nonlocality of neural network skills", |
| "authors": ["Eric Michaud", "Asher Parker-Sartori", "Max Tegmark"], |
| "upload_date": "2024-06-22", |
| "upload_method": "HuggingFace CLI" |
| }, |
| "experiments": { |
| "trainscratch01": { |
| "description": "LLMs trained from scratch on GitHub code", |
| "purpose": "Scaling analysis for paper Figures 6 & 12", |
| "dataset": "codeparrot/github-code (Python subset)", |
| "training_steps": 100000, |
| "learning_rate": "5e-4", |
| "sequence_length": 1024, |
| "hardware": "NVIDIA A100 80GB" |
| } |
| }, |
| "models_uploaded": { |
| "trainscratch01/d256_l4_h4": { |
| "parameters": "23M", |
| "hidden_size": 256, |
| "num_layers": 4, |
| "num_heads": 4, |
| "intermediate_size": 1024, |
| "model_size_gb": 0.15, |
| "purpose": "Smallest model for scaling baseline" |
| }, |
| "trainscratch01/d768_l12_h12": { |
| "parameters": "338M", |
| "hidden_size": 768, |
| "num_layers": 12, |
| "num_heads": 12, |
| "intermediate_size": 3072, |
| "model_size_gb": 0.65, |
| "purpose": "Representative medium model for key scaling point" |
| }, |
| "trainscratch01/d1024_l16_h16": { |
| "parameters": "~500M", |
| "hidden_size": 1024, |
| "num_layers": 16, |
| "num_heads": 16, |
| "intermediate_size": 4096, |
| "model_size_gb": 1.13, |
| "purpose": "Alternative medium size for scaling comparison" |
| } |
| }, |
| "usage": { |
| "loading_models": { |
| "library": "transformers", |
| "example": "AutoModelForCausalLM.from_pretrained('ericjm/narrow-data', subfolder='trainscratch01/d768_l12_h12/final_model')" |
| }, |
| "tokenizer": { |
| "compatible": "NousResearch/Meta-Llama-3.1-8B", |
| "note": "Use this tokenizer for compatibility with all models" |
| }, |
| "training_curves": { |
| "location": "trainer_state.json within each final_model directory", |
| "description": "Contains step-by-step training history and loss curves" |
| } |
| }, |
| "paper_figures": { |
| "Figure 6": "LLM training frontiers - uses scaling analysis from these models", |
| "Figure 12": "Training run comparison - compares training efficiency across model sizes" |
| }, |
| "technical_details": { |
| "model_format": "SafeTensors", |
| "precision": "float32", |
| "total_upload_size_gb": 1.93, |
| "files_per_model": ["model.safetensors", "config.json", "tokenizer.json", "trainer_state.json", "training_args.bin"], |
| "excluded_files": ["pruning_mask.pt (5GB each)", "large intermediate checkpoints"], |
| "optimization": "Essential final models only for efficient sharing" |
| }, |
| "citation": { |
| "bibtex": "@article{michaud2024narrow, title={On the creation of narrow AI: hierarchy and nonlocality of neural network skills}, author={Michaud, Eric and Parker-Sartori, Asher and Tegmark, Max}, journal={arXiv preprint}, year={2024}}" |
| } |
| } |
