# Rosie Custom Model Design

## Architecture Overview

**Model Type:** Custom Transformer-based Language Model
**Size:** Small (~500M-1B parameters)
**Framework:** PyTorch
**Training:** From scratch
**Personality:** Playful Assistant/Friend

## Model Specifications

### Architecture
- **Type:** Decoder-only Transformer (GPT-style)
- **Layers:** 12-16 transformer blocks
- **Hidden Size:** 768-1024
- **Attention Heads:** 12-16
- **Context Window:** 2048 tokens
- **Vocabulary Size:** 32k tokens (BPE tokenizer)

### Special Features
1. **Emotion Head:** Separate classification head for emotion detection
2. **Memory Attention:** Special attention mechanism for long-term memory
3. **Personality Embedding:** Learned embeddings for consistent personality traits

## Training Strategy

### Phase 1: Base Language Understanding
**Data Sources:**
- Common Crawl (filtered for appropriate content)
- Books corpus
- Reddit conversations (filtered)
- Estimated tokens: 10-50B

**Goal:** Learn basic language, grammar, world knowledge

### Phase 2: Personality Fine-tuning
**Data Sources:**
- Custom dialogue dataset (we'll create)
- Anime/VTuber transcripts (playful personality)
- Assistant conversations (helpful responses)
- Estimated examples: 100k-500k conversations

**Goal:** Develop Rosie's playful assistant personality

### Phase 3: Emotion & Memory Training
**Data Sources:**
- Conversations labeled with emotions
- Multi-turn dialogues with context
- Estimated examples: 50k-100k

**Goal:** Emotion detection and contextual memory

## Data Collection Plan

### What We Need to Create

1. **Personality Dataset (~10k examples)**
   - Playful greetings
   - Helpful responses
   - Reactions to being touched/moved
   - Idle conversation starters
   - Emotional responses

2. **Conversation Templates**
   - User: "Hello!"
   - Rosie: "Hey there! ✨ What's up?"

   - User: *drags Rosie*
   - Rosie: "Eep! 💕 Where are we going?"

   - User: "How are you?"
   - Rosie: "I'm doing great! Ready to help with whatever you need~"

3. **Emotion Labels**
   - Map responses to emotion states (happy, sad, surprised, etc.)
   - Train emotion classifier alongside text generation

## Training Hardware Requirements

### Your Setup (12GB VRAM)
- ✅ Can train 500M model with batch size 4-8
- ✅ Use gradient accumulation for effective larger batches
- ✅ Mixed precision training (FP16)
- ⚠️ May need gradient checkpointing for 1B model

### Estimated Training Time
- Phase 1 (base): 3-7 days on single GPU
- Phase 2 (personality): 1-2 days
- Phase 3 (emotion): 6-12 hours

## Model Files Structure

```
models/
├── rosie_model/
│   ├── config.json          # Model architecture config
│   ├── tokenizer/           # BPE tokenizer files
│   ├── weights/
│   │   ├── base.pth         # Base language model
│   │   ├── personality.pth  # Fine-tuned personality
│   │   └── final.pth        # Final trained model
│   └── checkpoints/         # Training checkpoints
```

## Implementation Plan

### Step 1: Create Model Architecture
- Custom transformer implementation
- Emotion classification head
- Memory attention mechanism

### Step 2: Create Tokenizer
- Train BPE tokenizer on diverse text
- 32k vocab size
- Special tokens for emotions/actions

### Step 3: Data Pipeline
- Download/prepare base training data
- Create custom personality dataset
- Build efficient data loaders

### Step 4: Training Loop
- Implement training script
- Add logging (wandb/tensorboard)
- Checkpoint management
- Evaluation metrics

### Step 5: Integration
- Load model in app
- Inference optimization (quantization, caching)
- Real-time response generation

## Alternative: Bootstrap Approach

If training from scratch takes too long, we can:
1. Start with a small pre-trained model (Phi-2, TinyLlama)
2. Fine-tune heavily on personality data
3. Add emotion head on top
4. Much faster (hours instead of days)

**Recommendation:** Start with bootstrap approach, transition to full custom model later if needed.

## Next Steps

1. Choose approach (from-scratch vs bootstrap)
2. Set up training environment
3. Create initial personality dataset
4. Implement model architecture
5. Begin training

What do you think? Should we go full custom from scratch, or bootstrap from a small existing model?