{
  "_comment": "Base models tested with RAE training methodology. Ordered by recommendation.",
  "models": [
    {
      "id": "Qwen/Qwen2.5-7B-Instruct",
      "params": "7B",
      "recommended_hardware": "1x A100-40GB or 1x A10G (with int4)",
      "rae_fit": "excellent",
      "notes": "Strong baseline reasoning. Best balance of capability and trainability. Recommended default.",
      "lora_r": 32,
      "lr": 5e-6,
      "block_size": 4096
    },
    {
      "id": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
      "params": "1.7B",
      "recommended_hardware": "1x T4 (even with fp16)",
      "rae_fit": "good",
      "notes": "Fastest iteration. Use for methodology validation before scaling. Can train on free Colab/Kaggle.",
      "lora_r": 16,
      "lr": 1e-5,
      "block_size": 2048
    },
    {
      "id": "meta-llama/Llama-3.2-3B-Instruct",
      "params": "3B",
      "recommended_hardware": "1x T4 (with int4)",
      "rae_fit": "good",
      "notes": "Good middle ground. Strong instruction following helps with phase structure adherence.",
      "lora_r": 16,
      "lr": 8e-6,
      "block_size": 4096
    },
    {
      "id": "mistralai/Mistral-7B-Instruct-v0.3",
      "params": "7B",
      "recommended_hardware": "1x A100-40GB or 1x A10G (with int4)",
      "rae_fit": "good",
      "notes": "Fast inference. Sliding window attention handles long RAE chains well.",
      "lora_r": 32,
      "lr": 5e-6,
      "block_size": 4096
    },
    {
      "id": "google/gemma-2-9b-it",
      "params": "9B",
      "recommended_hardware": "1x A100-80GB or 1x A10G (with int4)",
      "rae_fit": "excellent",
      "notes": "Strong reasoning baseline. Larger capacity absorbs RAE structure well.",
      "lora_r": 32,
      "lr": 3e-6,
      "block_size": 4096
    },
    {
      "id": "Qwen/Qwen2.5-3B-Instruct",
      "params": "3B",
      "recommended_hardware": "1x T4",
      "rae_fit": "good",
      "notes": "Excellent for cost-efficient experimentation. Strong reasoning for size.",
      "lora_r": 16,
      "lr": 8e-6,
      "block_size": 4096
    }
  ],
  "hardware_tiers": {
    "free": {
      "gpu": "T4 16GB (Kaggle/Colab free)",
      "max_model": "3B (int4) or 1.7B (fp16)",
      "training_speed": "~50 examples/hour",
      "recommended_model": "HuggingFaceTB/SmolLM2-1.7B-Instruct"
    },
    "budget": {
      "gpu": "A10G 24GB (HF Spaces ~$1.05/hr)",
      "max_model": "7B (int4)",
      "training_speed": "~150 examples/hour",
      "recommended_model": "Qwen/Qwen2.5-7B-Instruct"
    },
    "standard": {
      "gpu": "A100 40GB (HF Spaces ~$4.13/hr)",
      "max_model": "13B (int4) or 7B (fp16)",
      "training_speed": "~400 examples/hour",
      "recommended_model": "Qwen/Qwen2.5-7B-Instruct"
    },
    "premium": {
      "gpu": "A100 80GB",
      "max_model": "70B (int4) or 13B (fp16)",
      "training_speed": "~600 examples/hour",
      "recommended_model": "google/gemma-2-9b-it"
    }
  }
}