minimind/configs/searchlm_profiles.json

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{
"SearchLM-100M": {
"hidden_size": 768,
"num_hidden_layers": 12,
"num_attention_heads": 8,
"num_key_value_heads": 4,
"attention_type": "gqa",
"intermediate_size": 2432,
"max_seq_len": 1024,
"usage": "debug, smoke tests, architecture ablation"
},
"SearchLM-300M": {
"hidden_size": 1024,
"num_hidden_layers": 24,
"num_attention_heads": 8,
"num_key_value_heads": 4,
"attention_type": "mla",
"intermediate_size": 3264,
"kv_lora_rank": 192,
"q_lora_rank": 384,
"max_seq_len": 1536,
"usage": "main resume model for SearchShortQA SFT + GRPO"
},
"SearchLM-500M": {
"hidden_size": 1280,
"num_hidden_layers": 28,
"num_attention_heads": 10,
"num_key_value_heads": 5,
"attention_type": "mla",
"intermediate_size": 4096,
"kv_lora_rank": 256,
"q_lora_rank": 512,
"max_seq_len": 2048,
"usage": "larger optional run if 7x3090 memory permits"
},
"Qwen-LoRA-Control": {
"base_model": "Qwen2.5-1.5B-or-3B",
"training": "LoRA or QLoRA only",
"usage": "open-source large model baseline, not trained from scratch"
}
}