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)
# 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)
# 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)
# DeepSeek Coder 33B (INT4 quantized)
hub search deepseek-ai/deepseek-coder-33b-base
44
4. Test on ARC Task
# 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):
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:
GPTQINT44bitbnb-4bit(bitsandbytes)GGUF(for llama.cpp compatibility)
RELAY COMMANDS CHEAT SHEET
# Search for model
hub search <query>
# Plug model by number from search results
<number>
# Plug model directly
plug <model_id>
# Plug to specific slot
plug slot N <model_id>
# 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