| { | |
| "model_type": "nanochat-lfm2-quantum-gpt", | |
| "architecture": "LFM2-style hybrid quantum GPT (nanochat.gpt.GPT)", | |
| "checkpoint_step": 2162, | |
| "val_bpb": 0.9221935981158367, | |
| "sequence_len": 1024, | |
| "vocab_size": 32768, | |
| "n_layer": 16, | |
| "n_head": 16, | |
| "n_kv_head": 8, | |
| "n_embd": 1024, | |
| "head_dim": 64, | |
| "mlp_type": "quantum", | |
| "quantum_num_qubits": 4, | |
| "quantum_depth": 2, | |
| "window_pattern": "L", | |
| "mixer_pattern": "CCCCCCCCCCAAAAAA", | |
| "conv_kernel": 3, | |
| "rope_theta": 1000000.0, | |
| "ffn_pattern": "", | |
| "use_value_embeddings": false, | |
| "note": "This is NOT a transformers AutoModel config -- the architecture is custom (see bundled nanochat/gpt.py). Load with nanochat.checkpoint_manager.build_model() as shown in inference.py." | |
| } | |