Dani
c7ce0085fb
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>
153 lines
4.2 KiB
Markdown
153 lines
4.2 KiB
Markdown
# Rosie Custom Model Design
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## Architecture Overview
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**Model Type:** Custom Transformer-based Language Model
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**Size:** Small (~500M-1B parameters)
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**Framework:** PyTorch
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**Training:** From scratch
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**Personality:** Playful Assistant/Friend
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## Model Specifications
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### Architecture
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- **Type:** Decoder-only Transformer (GPT-style)
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- **Layers:** 12-16 transformer blocks
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- **Hidden Size:** 768-1024
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- **Attention Heads:** 12-16
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- **Context Window:** 2048 tokens
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- **Vocabulary Size:** 32k tokens (BPE tokenizer)
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### Special Features
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1. **Emotion Head:** Separate classification head for emotion detection
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2. **Memory Attention:** Special attention mechanism for long-term memory
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3. **Personality Embedding:** Learned embeddings for consistent personality traits
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## Training Strategy
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### Phase 1: Base Language Understanding
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**Data Sources:**
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- Common Crawl (filtered for appropriate content)
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- Books corpus
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- Reddit conversations (filtered)
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- Estimated tokens: 10-50B
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**Goal:** Learn basic language, grammar, world knowledge
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### Phase 2: Personality Fine-tuning
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**Data Sources:**
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- Custom dialogue dataset (we'll create)
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- Anime/VTuber transcripts (playful personality)
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- Assistant conversations (helpful responses)
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- Estimated examples: 100k-500k conversations
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**Goal:** Develop Rosie's playful assistant personality
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### Phase 3: Emotion & Memory Training
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**Data Sources:**
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- Conversations labeled with emotions
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- Multi-turn dialogues with context
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- Estimated examples: 50k-100k
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**Goal:** Emotion detection and contextual memory
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## Data Collection Plan
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### What We Need to Create
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1. **Personality Dataset (~10k examples)**
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- Playful greetings
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- Helpful responses
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- Reactions to being touched/moved
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- Idle conversation starters
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- Emotional responses
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2. **Conversation Templates**
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- User: "Hello!"
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- Rosie: "Hey there! ✨ What's up?"
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- User: *drags Rosie*
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- Rosie: "Eep! 💕 Where are we going?"
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- User: "How are you?"
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- Rosie: "I'm doing great! Ready to help with whatever you need~"
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3. **Emotion Labels**
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- Map responses to emotion states (happy, sad, surprised, etc.)
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- Train emotion classifier alongside text generation
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## Training Hardware Requirements
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### Your Setup (12GB VRAM)
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- ✅ Can train 500M model with batch size 4-8
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- ✅ Use gradient accumulation for effective larger batches
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- ✅ Mixed precision training (FP16)
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- ⚠️ May need gradient checkpointing for 1B model
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### Estimated Training Time
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- Phase 1 (base): 3-7 days on single GPU
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- Phase 2 (personality): 1-2 days
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- Phase 3 (emotion): 6-12 hours
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## Model Files Structure
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```
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models/
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├── rosie_model/
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│ ├── config.json # Model architecture config
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│ ├── tokenizer/ # BPE tokenizer files
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│ ├── weights/
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│ │ ├── base.pth # Base language model
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│ │ ├── personality.pth # Fine-tuned personality
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│ │ └── final.pth # Final trained model
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│ └── checkpoints/ # Training checkpoints
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```
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## Implementation Plan
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### Step 1: Create Model Architecture
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- Custom transformer implementation
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- Emotion classification head
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- Memory attention mechanism
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### Step 2: Create Tokenizer
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- Train BPE tokenizer on diverse text
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- 32k vocab size
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- Special tokens for emotions/actions
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### Step 3: Data Pipeline
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- Download/prepare base training data
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- Create custom personality dataset
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- Build efficient data loaders
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### Step 4: Training Loop
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- Implement training script
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- Add logging (wandb/tensorboard)
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- Checkpoint management
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- Evaluation metrics
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### Step 5: Integration
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- Load model in app
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- Inference optimization (quantization, caching)
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- Real-time response generation
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## Alternative: Bootstrap Approach
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If training from scratch takes too long, we can:
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1. Start with a small pre-trained model (Phi-2, TinyLlama)
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2. Fine-tune heavily on personality data
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3. Add emotion head on top
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4. Much faster (hours instead of days)
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**Recommendation:** Start with bootstrap approach, transition to full custom model later if needed.
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## Next Steps
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1. Choose approach (from-scratch vs bootstrap)
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2. Set up training environment
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3. Create initial personality dataset
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4. Implement model architecture
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5. Begin training
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What do you think? Should we go full custom from scratch, or bootstrap from a small existing model?
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