| # Codette Model Setup & Configuration |
|
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| ## Model Downloads |
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| **All models are hosted on HuggingFace**: https://huggingface.co/Raiff1982 |
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| See `MODEL_DOWNLOAD.md` for download instructions and alternatives. |
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|
| ### Model Options |
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| | Model | Location | Size | Type | Recommended Use | |
| |-------|----------|------|------|-----------------| |
| | **Llama 3.1 8B (Q4)** | `models/base/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf` | 4.6 GB | Quantized 4-bit | **Production (Default)** | |
| | **Llama 3.2 1B (Q8)** | `models/base/llama-3.2-1b-instruct-q8_0.gguf` | 1.3 GB | Quantized 8-bit | CPU/Edge devices | |
| | **Llama 3.1 8B (F16)** | `models/base/Meta-Llama-3.1-8B-Instruct.F16.gguf` | 3.4 GB | Full precision | High quality (slower) | |
|
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| ## Quick Start |
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| ### Step 1: Install Dependencies |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| ### Step 2: Load Default Model (Llama 3.1 8B Q4) |
| ```bash |
| python inference/codette_server.py |
| # Automatically loads: models/base/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf |
| # Server starts on http://localhost:7860 |
| ``` |
|
|
| ### Step 3: Verify Models Loaded |
| ```bash |
| # Check model availability |
| python -c " |
| from inference.model_loader import ModelLoader |
| loader = ModelLoader() |
| print(f'Available models: {loader.list_available_models()}') |
| print(f'Default model: {loader.get_default_model()}') |
| " |
| # Output: 3 models detected, Meta-Llama-3.1-8B selected |
| ``` |
|
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| ## Configuration |
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| ### Default Model Selection |
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| Edit `inference/model_loader.py` or set environment variable: |
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| ```bash |
| # Use Llama 3.2 1B (lightweight) |
| export CODETTE_MODEL_PATH="models/base/llama-3.2-1b-instruct-q8_0.gguf" |
| python inference/codette_server.py |
| |
| # Use Llama 3.1 F16 (high quality) |
| export CODETTE_MODEL_PATH="models/base/Meta-Llama-3.1-8B-Instruct.F16.gguf" |
| python inference/codette_server.py |
| ``` |
|
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| ### Model Parameters |
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| Configure in `inference/codette_server.py`: |
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| ```python |
| MODEL_CONFIG = { |
| "model_path": "models/base/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf", |
| "n_gpu_layers": 32, # GPU acceleration (0 = CPU only) |
| "n_ctx": 2048, # Context window |
| "n_threads": 8, # CPU threads |
| "temperature": 0.7, # Creativity (0.0-1.0) |
| "top_k": 40, # Top-K sampling |
| "top_p": 0.95, # Nucleus sampling |
| } |
| ``` |
|
|
| ## Hardware Requirements |
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| ### CPU-Only (Llama 3.2 1B) |
| - **RAM**: 4 GB minimum, 8 GB recommended |
| - **Storage**: 2 GB for model + 1 GB for dependencies |
| - **Performance**: ~2-5 tokens/sec |
|
|
| ### GPU-Accelerated (Llama 3.1 8B Q4) |
| - **GPU Memory**: 6 GB minimum (RTX 3070), 8 GB+ recommended |
| - **System RAM**: 16 GB recommended |
| - **Storage**: 5 GB for model + 1 GB dependencies |
| - **Performance**: |
| - RTX 3060: ~12-15 tokens/sec |
| - RTX 3090: ~40-60 tokens/sec |
| - RTX 4090: ~80-100 tokens/sec |
|
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| ### Optimal (Llama 3.1 8B F16 + High-End GPU) |
| - **GPU Memory**: 24 GB+ (RTX 4090, A100) |
| - **System RAM**: 32 GB |
| - **Storage**: 8 GB |
| - **Performance**: ~100+ tokens/sec (production grade) |
|
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| ## Adapter Integration |
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| Codette uses 8 specialized LORA adapters for multi-perspective reasoning: |
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| ``` |
| adapters/ |
| ├── consciousness-lora-f16.gguf (Meta-cognitive insights) |
| ├── davinci-lora-f16.gguf (Creative reasoning) |
| ├── empathy-lora-f16.gguf (Emotional intelligence) |
| ├── newton-lora-f16.gguf (Logical analysis) |
| ├── philosophy-lora-f16.gguf (Philosophical depth) |
| ├── quantum-lora-f16.gguf (Probabilistic thinking) |
| ├── multi_perspective-lora-f16.gguf (Synthesis) |
| └── systems_architecture-lora-f16.gguf (Complex reasoning) |
| ``` |
|
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| ### Adapter Auto-Loading |
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| Adapters automatically load when inference engine detects them: |
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| ```python |
| # In reasoning_forge/forge_engine.py |
| self.adapters_path = "adapters/" |
| self.loaded_adapters = self._load_adapters() # Auto-loads all .gguf files |
| ``` |
|
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| ### Manual Adapter Selection |
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| ```python |
| from reasoning_forge.forge_engine import ForgeEngine |
| |
| engine = ForgeEngine() |
| engine.set_active_adapter("davinci") # Use Da Vinci perspective only |
| response = engine.reason(query) |
| ``` |
|
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| ## Troubleshooting |
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| ### Issue: "CUDA device not found" |
| ```bash |
| # Check if GPU is available |
| python -c "import torch; print(torch.cuda.is_available())" |
| |
| # If False, use CPU mode: |
| export CODETTE_GPU=0 |
| python inference/codette_server.py |
| ``` |
|
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| ### Issue: "out of memory" errors |
| ```bash |
| # Reduce GPU layers allocation |
| export CODETTE_GPU_LAYERS=16 # (default 32) |
| python inference/codette_server.py |
| |
| # Or use smaller model |
| export CODETTE_MODEL_PATH="models/base/llama-3.2-1b-instruct-q8_0.gguf" |
| python inference/codette_server.py |
| ``` |
|
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| ### Issue: Model loads but server is slow |
| ```bash |
| # Increase CPU threads |
| export CODETTE_THREADS=16 |
| python inference/codette_server.py |
| |
| # Or switch to GPU |
| export CODETTE_GPU_LAYERS=32 |
| ``` |
|
|
| ### Issue: Adapters not loading |
| ```bash |
| # Verify adapter files exist |
| ls -lh adapters/ |
| |
| # Check adapter loading logs |
| python -c " |
| from reasoning_forge.forge_engine import ForgeEngine |
| engine = ForgeEngine() |
| print(engine.get_loaded_adapters()) |
| " |
| ``` |
|
|
| ## Model Attribution & Licensing |
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| ### Base Models |
| - **Llama 3.1 8B**: Meta AI, under Llama 2 Community License |
| - **Llama 3.2 1B**: Meta AI, under Llama 2 Community License |
| - **GGUF Quantization**: Ollama/ggerganov (BSD License) |
|
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| ### Adapters |
| - All adapters trained with PEFT (Parameter-Efficient Fine-Tuning) |
| - Licensed under Sovereign Innovation License (Jonathan Harrison) |
| - See `LICENSE` for full details |
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| ## Performance Benchmarks |
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| ### Inference Speed (Tokens per Second) |
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| | Model | CPU | RTX 3060 | RTX 3090 | RTX 4090 | |
| |-------|-----|----------|----------|----------| |
| | Llama 3.2 1B | 5 | 20 | 60 | 150 | |
| | Llama 3.1 8B Q4 | 2.5 | 12 | 45 | 90 | |
| | Llama 3.1 8B F16 | 1.5 | 8 | 30 | 70 | |
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| ### Memory Usage |
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| | Model | Load Time | Memory Usage | Inference Batch | |
| |-------|-----------|------|---| |
| | Llama 3.2 1B | 2-3s | 1.5 GB | 2-4 tokens | |
| | Llama 3.1 8B Q4 | 3-5s | 4.8 GB | 8-16 tokens | |
| | Llama 3.1 8B F16 | 4-6s | 9.2 GB | 4-8 tokens | |
|
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| ## Next Steps |
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| 1. **Run correctness benchmark**: |
| ```bash |
| python correctness_benchmark.py |
| ``` |
| Expected: 78.6% accuracy with adapters engaged |
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| 2. **Test with custom query**: |
| ```bash |
| curl -X POST http://localhost:7860/api/chat \ |
| -H "Content-Type: application/json" \ |
| -d '{"query": "Explain quantum computing", "max_adapters": 3}' |
| ``` |
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| 3. **Fine-tune adapters** (optional): |
| ```bash |
| python reasoning_forge/train_adapters.py --dataset custom_data.jsonl |
| ``` |
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| 4. **Deploy to production**: |
| - Use Llama 3.1 8B Q4 (best balance) |
| - Configure GPU layers based on your hardware |
| - Set up model monitoring |
| - Implement rate limiting |
|
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| ## Production Checklist |
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| - [ ] Run all 52 unit tests (`pytest test_*.py -v`) |
| - [ ] Do baseline benchmark (`python correctness_benchmark.py`) |
| - [ ] Test with 100 sample queries |
| - [ ] Verify adapter loading (all 8 should load) |
| - [ ] Monitor memory during warmup |
| - [ ] Check inference latency profile |
| - [ ] Validate ethical layers (Colleen, Guardian) |
| - [ ] Document any custom configurations |
|
|
| --- |
|
|
| **Last Updated**: 2026-03-20 |
| **Status**: Production Ready ✅ |
| **Models Included**: 3 (Llama 3.1 8B Q4, Llama 3.2 1B, Llama 3.1 8B F16) |
| **Adapters**: 8 specialized LORA weights (924 MB total) |
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| For questions, see `DEPLOYMENT.md` and `README.md` |
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