# MODELS TO PLUG - ARC-AGI 48-SLOT ARCHITECTURE **Ready-to-use HuggingFace model IDs for relay system** --- ## TIER 1: PERCEPTION BANK (8 slots) ### Slot 0: SigLIP (Patch-level vision) ``` hub search google/siglip-base-patch16-224 1 ``` **Model**: `google/siglip-base-patch16-224` **VRAM**: ~0.8 GB ### Slot 1: CLIP (Global vision) ``` hub search openai/clip-vit-large-patch14 1 ``` **Model**: `openai/clip-vit-large-patch14` **VRAM**: ~0.8 GB ### Slot 2: DINOv2 (Self-supervised) ``` hub search facebook/dinov2-base 2 ``` **Model**: `facebook/dinov2-base` **VRAM**: ~0.6 GB ### Slot 3: SAM Encoder (Segmentation) ``` hub search facebook/sam-vit-base 1 ``` **Model**: `facebook/sam-vit-base` **VRAM**: ~1.2 GB ### Slot 4: DETR (Object detection) ``` hub search facebook/detr-resnet-50 1 ``` **Model**: `facebook/detr-resnet-50` **VRAM**: ~0.1 GB ### Slots 5-7: Custom models (TBD) - Edge detector (custom CNN) - Color quantizer (custom) - Grid parser (custom transformer) **TIER 1 TOTAL**: ~3.7 GB --- ## TIER 2: PRIMITIVE SPECIALISTS (16 slots) **Strategy**: Fine-tune small models (DistilBERT, Small ViT) for each primitive ### Base models to fine-tune: ``` hub search distilbert-base-uncased hub search google/vit-base-patch16-224 hub search microsoft/resnet-50 ``` **Per specialist**: ~0.3B params, ~0.6 GB VRAM **TIER 2 TOTAL**: ~9.6 GB --- ## TIER 3: PROGRAM SYNTHESIS (8 slots) ### Slot 24: DeepSeek Coder 33B ``` hub search deepseek-ai/deepseek-coder-33b-base 44 ``` **Model**: `deepseek-ai/deepseek-coder-33b-base` **VRAM**: 66 GB (FP16) / 16.5 GB (INT4) ### Slot 25: CodeLlama 34B ``` hub search codellama/CodeLlama-34b-hf 1 ``` **Model**: `codellama/CodeLlama-34b-hf` **VRAM**: 68 GB (FP16) / 17 GB (INT4) ### Slot 26: StarCoder2 15B ``` hub search bigcode/starcoder2-15b 1 ``` **Model**: `bigcode/starcoder2-15b` **VRAM**: 30 GB (FP16) / 7.5 GB (INT4) ### Slot 27: Qwen2-Coder 32B ``` hub search Qwen/Qwen2.5-Coder-32B-Instruct 1 ``` **Model**: `Qwen/Qwen2.5-Coder-32B-Instruct` **VRAM**: 64 GB (FP16) / 16 GB (INT4) ### Slot 28: DSL Generator (Fine-tuned CodeLlama 7B) ``` hub search codellama/CodeLlama-7b-hf 1 ``` **Model**: `codellama/CodeLlama-7b-hf` (to be fine-tuned) **VRAM**: 14 GB (FP16) / 3.5 GB (INT4) ### Slot 29: Tree Builder (Custom) TBD - Custom transformer for AST generation ### Slot 30: Genetic Programmer ``` hub search mistralai/Mistral-7B-v0.1 1 ``` **Model**: `mistralai/Mistral-7B-v0.1` **VRAM**: 14 GB (FP16) / 3.5 GB (INT4) ### Slot 31: Analogy Mapper ``` hub search BAAI/bge-large-en-v1.5 1 ``` **Model**: `BAAI/bge-large-en-v1.5` **VRAM**: 2 GB **TIER 3 TOTAL**: 264 GB (FP16) / 66 GB (INT4) --- ## TIER 4: REASONING & INDUCTION (6 slots) ### Slot 32: Llama 3.1 70B ``` hub search meta-llama/Llama-3.1-70B-Instruct 1 ``` **Model**: `meta-llama/Llama-3.1-70B-Instruct` **VRAM**: 140 GB (FP16) / 35 GB (INT4) ### Slot 33: Claude-distill (Proxy with smaller model) ``` hub search mistralai/Mistral-7B-Instruct-v0.3 1 ``` **Model**: `mistralai/Mistral-7B-Instruct-v0.3` **VRAM**: 14 GB (FP16) / 3.5 GB (INT4) ### Slot 34: Qwen2-Math 72B ``` hub search Qwen/Qwen2.5-Math-72B-Instruct 1 ``` **Model**: `Qwen/Qwen2.5-Math-72B-Instruct` **VRAM**: 144 GB (FP16) / 36 GB (INT4) ### Slot 35: Gemma 2 27B ``` hub search google/gemma-2-27b-it 1 ``` **Model**: `google/gemma-2-27b-it` **VRAM**: 54 GB (FP16) / 13.5 GB (INT4) ### Slot 36: Rule Inducer (Fine-tuned Llama 13B) ``` hub search meta-llama/Llama-2-13b-hf 1 ``` **Model**: `meta-llama/Llama-2-13b-hf` (to be fine-tuned) **VRAM**: 26 GB (FP16) / 6.5 GB (INT4) ### Slot 37: Hypothesis Generator ``` hub search mistralai/Mistral-7B-Instruct-v0.3 1 ``` **Model**: `mistralai/Mistral-7B-Instruct-v0.3` **VRAM**: 14 GB (FP16) / 3.5 GB (INT4) **TIER 4 TOTAL**: 392 GB (FP16) / 98 GB (INT4) --- ## TIER 5: VERIFICATION (4 slots) ### Slots 38-41: Lightweight verification tools - Python executor (built-in) - Similarity scorer (numpy/sklearn) - Z3 solver (symbolic verification) - Confidence scorer (small classifier) **TIER 5 TOTAL**: ~1.8 GB --- ## TIER 6: SEARCH & META (4 slots) ### Slot 44: Task Classifier ``` hub search distilbert-base-uncased 1 ``` **Model**: `distilbert-base-uncased` (to be fine-tuned) **VRAM**: ~0.5 GB ### Slots 42-43, 45: Custom algorithms - MCTS controller (Python) - Beam ranker (small scorer) - Difficulty estimator (regressor) **TIER 6 TOTAL**: ~2.3 GB --- ## TIER 7: MEMORY & RETRIEVAL (2 slots) ### Slot 46: Task Embedder ``` hub search BAAI/bge-large-en-v1.5 1 ``` **Model**: `BAAI/bge-large-en-v1.5` **VRAM**: ~0.6 GB ### Slot 47: Solution Bank - FAISS vector database - Populated with 400 ARC training tasks **TIER 7 TOTAL**: ~2.6 GB --- ## QUICK START SEQUENCE (After GPU rental) ### 1. Load Core Infrastructure (< 10 GB) ```bash # Perception hub search google/siglip-base-patch16-224 1 hub search openai/clip-vit-large-patch14 1 hub search facebook/dinov2-base 2 # Memory hub search BAAI/bge-large-en-v1.5 1 # Task classifier hub search distilbert-base-uncased 1 ``` ### 2. Load Reasoning Model (+ 35 GB) ```bash # Llama 3.1 70B (INT4 quantized) hub search meta-llama/Llama-3.1-70B-Instruct # Look for INT4/GPTQ version ``` ### 3. Load Synthesis Model (swap with reasoning) ```bash # DeepSeek Coder 33B (INT4 quantized) hub search deepseek-ai/deepseek-coder-33b-base 44 ``` ### 4. Test on ARC Task ```bash # Load ARC task infer "Analyze this grid pattern..." # Run through pipeline deliberate "What transformation is needed?" ``` --- ## QUANTIZED MODEL SEARCH TIPS For INT4/GPTQ versions (4× smaller VRAM): ```bash hub search meta-llama/Llama-3.1-70B GPTQ hub search meta-llama/Llama-3.1-70B INT4 hub search deepseek-ai/deepseek-coder-33b GPTQ ``` Look for model IDs containing: - `GPTQ` - `INT4` - `4bit` - `bnb-4bit` (bitsandbytes) - `GGUF` (for llama.cpp compatibility) --- ## RELAY COMMANDS CHEAT SHEET ```bash # Search for model hub search # Plug model by number from search results # Plug model directly plug # Plug to specific slot plug slot N # Check what's loaded who # Check slots slots # Test model embed "test text" # Run inference infer "input text" # Council deliberation deliberate "question" # Dreamer imagination imagine 10 ``` --- *All models verified on HuggingFace Hub as of 2026-01-28* *Ready for ARC-AGI Prize competition*