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"input_dim": 512,
"hidden_dim": 2048,
"output_dim": 2048,
"memory_dim": 512,
"memory_slots": 64,
"num_layers": 12,
"num_associations": 8,
"use_quantization": false,
"use_mla": false,
"mla_latent_dim": 32,
"use_causal_lm": true,
"vocab_size": 128000,
"max_seq_len": 512,
"causal_window_size": 64,
"sae_k": 64,
"ntm_memory_slots": 16,
"d_model": 512,
"mla_n_heads": 8,
"mla_max_cache_len": 4096,
"lm_num_attn_layers": 2,
"lm_pooling": "mean",
"_comment": {
"input_dim": "输入维度(d_model)",
"hidden_dim": "隐藏层维度(FFN)",
"output_dim": "三脑内部输出维度(=hidden_dim, 非vocab_size)",
"memory_dim": "记忆维度(d_mem)",
"memory_slots": "记忆槽数(MEM_SLOTS)",
"num_layers": "网络层数(ECN层)",
"num_associations": "DMN关联头数",
"use_quantization": "启用量化",
"use_mla": "启用MLA",
"mla_latent_dim": "MLA潜在维度",
"use_causal_lm": "因果LM — next-token预测",
"vocab_size": "128K多语言多领域词表(中/英/代码/数字/标点)",
"max_seq_len": "最大序列长度",
"causal_window_size": "因果窗口大小",
"sae_k": "SAE稀疏度",
"ntm_memory_slots": "NTM记忆槽数",
"d_model": "模型维度(512)",
"mla_n_heads": "MLA头数",
"mla_max_cache_len": "MLA最大缓存长度",
"lm_num_attn_layers": "CausalLM注意力层数(2-4)",
"lm_pooling": "池化方式(mean/last)"
},
"_fix_log": "2026-06-13: 128K BPE tokenizer (train_128k_tokenizer.py)"
}
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