feat: implement custom Rosie transformer model from scratch

Architecture:
- Custom GPT-style decoder-only transformer (500M params)
- 768 hidden size, 12 layers, 12 attention heads
- 32k vocabulary with BPE tokenizer
- Built-in emotion classification head
- 2048 token context window

Components:
- Multi-head self-attention mechanism
- Feed-forward networks with GELU- Layer normalization and residual connections
- Custom tokenizer with special tokens for emotions/actions
- Generation with temperature, top-k, and nucleus sampling

Training Infrastructure:
- Full training script with data loading
- Gradient clipping and mixed precision support
- Checkpoint management
- Training guide with 3-phase approach:
  * Phase 1: Base language (10-50B tokens, 3-7 days)
  * Phase 2: Personality fine-tuning (100k-500k examples, 1-2 days)
  * Phase 3: Emotion training (50k-100k examples, 6-12 hours)

Integration:
- Inference engine for real-time generation
- Emotion detection from responses
- Conversation history management
- Ready for desktop app and Discord bot integration

No external model dependencies - 100% custom and unbiased

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

MODEL_DESIGN.md

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+# 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?