| { | |
| "architecture": "BigramLanguageModel", | |
| "architecture_description": "Token + position embeddings -> n_layer stacked transformer blocks (pre-norm multi-head causal self-attention + feedforward, residual connections) -> final LayerNorm -> linear head to vocab logits.", | |
| "vocab_size": 65, | |
| "n_embd": 128, | |
| "n_head": 4, | |
| "n_layer": 4, | |
| "block_size": 32, | |
| "tokenizer_type": "char", | |
| "tokenizer_file": "tokenizer_char.json", | |
| "weights_file": "model.safetensors", | |
| "training": { | |
| "dataset": "tinyshakespeare.txt", | |
| "batch_size": 32, | |
| "learning_rate": 0.001, | |
| "max_iters": 8000, | |
| "seed": 1337 | |
| }, | |
| "parameter_count": 812609 | |
| } | |