MODELS_TO_PLUG.md

# 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 <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*