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**Date**: 2025-04-02
**Status**: ✅ Complete
**Deliverables**: All 5 tasks fulfilled
---
## 1. MODEL_CARD.md ✅
**Location**: `stack-2.9/MODEL_CARD.md`
**Contents**:
- **Model Description**: Stack 2.9 as a fine-tuned Qwen2.5-Coder-32B with Pattern Memory
- **Training Data Sources**: Detailed breakdown of synthetic examples, pattern memory data, public datasets (OpenAssistant, CodeAct, CodeContests, StarCoder), code-comment pairs
- **Training Procedure**: LoRA fine-tuning with 3 epochs, gradient accumulation 16, learning rate 1e-4, 4-bit quantization, training pipeline steps (prepare_data.py, train_lora.py, merge_adapter.py)
- **Hyperparameters**: Complete configuration from train_config.yaml (r=64, alpha=128, dropout=0.05, target modules, max_length=131072, etc.)
- **Intended Uses**: AI-assisted coding, education, research; NOT for safety-critical or autonomous deployment
- **Limitations**: 128K context may degrade at max length, hallucinations possible, requires human oversight, tool dependencies on OpenClaw
- **License**: MIT License (fine-tuned code + model wrapper), Apache 2.0 (base Qwen model + training data)
---
## 2. inference_examples.py ✅
**Location**: `stack-2.9/inference_examples.py`
**Contents**: 15 diverse coding demonstration tasks covering:
1. Simple function (factorial recursion)
2. Data structure (LRU cache)
3. Code explanation (quicksort)
4. Debugging (find duplicates bug)
5. Refactoring (Pythonic list comprehension)
6. API integration (REST with retries)
7. File operations (pattern memory)
8. Multi-step workflow (project scaffolding)
9. System design (task queue)
10. Web development (Flask/FastAPI)
11. Code translation (JS to Python)
12. Unit testing (pytest for binary search)
13. Data processing (CSV aggregation)
14. Async programming (concurrent URL fetch)
15. Pattern retrieval (tree traversal)
**Features**:
- CLI with `--provider` supporting ollama, openai, anthropic, openrouter, together
- Uses model_client to run actual inference or documentation mode
- Reports token counts and latency
- Can be extended with more examples
---
## 3. README Badges ✅
**Updated**: `stack-2.9/README.md`
**Changes**:
- Added Together AI badge: `Together_AI-Supported-green`
- Updated Multi-Provider feature to include Together AI
- Added Together AI environment variables to Configuration section
- Evaluation badges remain as "Evaluation In Progress" (pending real scores)
**Note**: Real benchmark scores will be updated after evaluation completes. The infrastructure is in place.
---
## 4. Together AI Documentation ✅
**Files Created**:
- `stack-2.9/TOGETHER_AI.md` - Comprehensive guide (350+ lines)
**Contents**:
- Overview of Together AI integration
- Prerequisites and setup
- Environment variables: `TOGETHER_API_KEY`, `TOGETHER_MODEL`, `MODEL_PROVIDER`
- Recommended models: `togethercomputer/Qwen2.5-Coder-32B-Instruct` (primary), plus alternatives (Llama-3-70B, CodeLlama-34B)
- Usage examples: CLI, Python API, chat mode, tool calls
- Cost estimation table
- Performance considerations
- Error handling and retries
- Comparison with other providers
- Troubleshooting guide
- Security best practices
**Integration in model_client.py** (`stack-2.9/stack-2.9-eval/model_client.py`):
- Added `TogetherClient` class implementing OpenAI-compatible API
- Uses base URL `https://api.together.xyz/v1`
- Default model: `togethercomputer/Qwen2.5-Coder-32B-Instruct`
- Updated `create_model_client` factory to support `provider="together"`
- Updated CLI parser to include "together" option
---
## 5. License Compatibility Verification ✅
**File Created**: `stack-2.9/LICENSES.md`
**Verification Results**:
- ✅ **Project Code**: MIT License (permissive)
- ✅ **Base Model**: Qwen2.5-Coder-32B - Apache 2.0 (Alibaba)
- ✅ **Training Data**: Apache 2.0 (manifest.json)
- ✅ **Dependencies**: All MIT, Apache 2.0, or BSD (torch, transformers, peft, bitsandbytes, datasets, openai, anthropic, requests, etc.)
- ✅ **Public Datasets**: OpenAssistant (Apache 2.0), CodeAct (permissive), CodeContests (permissive), StarCoder (permissive)
**Conclusion**: All components licensed under permissive terms that allow redistribution, modification, and commercial use. Stack 2.9 can be distributed under MIT for code + Apache 2.0 for model/data.
---
## Additional Artifacts
- **MODEL_CARD.md** conforms to standard model card format (model description, training data, procedure, uses, limitations, license)
- **inference_examples.py** is executable and demonstrates real capabilities
- **TOGETHER_AI.md** provides complete coverage for Together AI deployment
- **LICENSES.md** provides legal clarity for distribution
---
## File Structure (new files)
```
stack-2.9/
├── MODEL_CARD.md (NEW)
├── TOGETHER_AI.md (NEW)
├── LICENSES.md (NEW)
├── inference_examples.py (NEW)
├── stack-2.9-eval/
│ └── model_client.py (MODIFIED - added TogetherClient)
├── README.md (MODIFIED - badges, config)
└── ... (existing files)
```
---
## Quick Start for Together AI Users
```bash
# 1. Set environment
export TOGETHER_API_KEY="tog-..."
export MODEL_PROVIDER="together"
export TOGETHER_MODEL="togethercomputer/Qwen2.5-Coder-32B-Instruct"
# 2. Run inference
python stack.py "Write a function to calculate fibonacci"
# Or run examples
python inference_examples.py --provider together
```
---
**All tasks completed successfully. The submission package is ready for Together AI integration.**
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