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--- |
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license: apache-2.0 |
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language: |
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- code |
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- en |
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language_bcp47: |
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- python |
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- javascript |
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- java |
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- cpp |
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- go |
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- rust |
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- typescript |
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- csharp |
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tags: |
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- code-generation |
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- programming-languages |
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- syntax-aware |
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- transformer |
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- code-understanding |
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- fine-tuning |
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- ast-guided |
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- code-completion |
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- software-engineering |
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- programming-assistant |
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pipeline_tag: text-generation |
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datasets: |
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- code_search_net |
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- github_code |
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library_name: transformers |
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base_model: transformer |
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model_type: sfm2 |
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inference: true |
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widget: |
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- text: 'def fibonacci(n):' |
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example_title: Python Function |
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- text: |- |
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// Calculate factorial |
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function factorial( |
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example_title: JavaScript Function |
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- text: |- |
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class DataProcessor { |
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public void process( |
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example_title: Java Class Method |
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- text: 'fn binary_search<T: Ord>(' |
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example_title: Rust Generic Function |
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--- |
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# SFM-2: Syntax-aware Foundation Model for Programming Languages |
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[](https://opensource.org/licenses/MIT) |
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[](https://www.python.org/downloads/) |
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[](https://huggingface.co/Bryantad/SfM-2) |
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[](https://arxiv.org/abs/2024.sfm2) |
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[](https://huggingface.co/spaces/Bryantad/SfM-2-Demo) |
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> **🧠 Revolutionary transformer architecture with syntax-aware attention mechanisms for next-generation programming language understanding and code generation** |
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## 🎯 Model Overview |
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SFM-2 (Syntax-aware Foundation Model 2) represents a breakthrough in AI-assisted programming. Unlike traditional language models that treat code as plain text, SFM-2 understands the structural and semantic relationships in programming languages through novel syntax-aware attention mechanisms. |
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### 🚀 Key Innovations |
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- 🧠 **Syntax-aware Attention**: First-of-its-kind attention mechanisms that understand programming language structure |
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- 🎯 **AST-guided Processing**: Leverages Abstract Syntax Trees for superior code understanding |
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- 🔄 **Multi-language Mastery**: Trained on 6+ programming languages with deep structural understanding |
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- ⚡ **Efficient Fine-tuning**: Advanced LoRA and parameter-efficient training methods |
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- 🛡️ **Production Ready**: Enterprise-grade API with intelligent fallback systems |
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- 🎓 **Research-backed**: Built on peer-reviewed research in cognitive accessibility and syntax-aware AI |
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## 🚀 Quick Start |
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### Using with Transformers 🤗 |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Load model and tokenizer |
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model_name = "Bryantad/SfM-2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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device_map="auto" |
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) |
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# Generate code with syntax awareness |
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prompt = "def fibonacci(n):" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model.generate( |
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inputs.input_ids, |
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max_length=150, |
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temperature=0.7, |
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do_sample=True, |
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pad_token_id=tokenizer.eos_token_id, |
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repetition_penalty=1.1 |
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) |
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generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_code) |
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``` |
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### 🎮 Interactive Demo |
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Try the model instantly in your browser: [🚀 Live Demo on Hugging Face Spaces](https://huggingface.co/spaces/Bryantad/SfM-2-Demo) |
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### 🔧 Advanced Usage |
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```python |
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# Function completion with context awareness |
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prompt = """ |
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class MathUtils: |
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@staticmethod |
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def gcd(a, b): |
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while b: |
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a, b = b, a % b |
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return a |
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@staticmethod |
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def lcm(a, b): |
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""" |
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# Code explanation and documentation |
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prompt = """ |
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# Explain this algorithm: |
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def quicksort(arr): |
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if len(arr) <= 1: |
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return arr |
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pivot = arr[len(arr) // 2] |
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left = [x for x in arr if x < pivot] |
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middle = [x for x in arr if x == pivot] |
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right = [x for x in arr if x > pivot] |
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return quicksort(left) + middle + quicksort(right) |
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# Explanation: |
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""" |
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# Multi-language code translation |
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prompt = """ |
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// JavaScript function |
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function factorial(n) { |
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return n <= 1 ? 1 : n * factorial(n - 1); |
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} |
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# Equivalent Python function: |
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""" |
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``` |
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## 🔧 Installation & Development |
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### 📦 System Requirements |
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- **Python**: 3.8+ (3.10+ recommended) |
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- **CUDA**: 11.8+ for GPU acceleration |
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- **Memory**: 16GB RAM minimum, 32GB recommended |
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- **Storage**: 50GB for full model weights |
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### 🚀 Local Development Setup |
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```bash |
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# Clone the repository |
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git clone https://github.com/Bryantad/SfM-2.git |
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cd SfM-2 |
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# Create virtual environment |
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python -m venv sfm2-env |
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source sfm2-env/bin/activate # On Windows: sfm2-env\Scripts\activate |
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# Install dependencies |
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pip install -r requirements.txt |
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# Verify installation |
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python -c "from src.sfm2.core.model import SFM2Model; print('✅ SFM-2 installed successfully')" |
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# Run training pipeline (optional) |
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python src/sfm2/training/pipeline.py --config configs/base_config.json |
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# Start API server |
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python src/sfm2/api/app.py --host 0.0.0.0 --port 8000 |
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``` |
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### 🐳 Docker Deployment |
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```bash |
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# Build container |
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docker build -t sfm2:latest . |
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# Run with GPU support |
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docker run --gpus all -p 8000:8000 sfm2:latest |
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# Production deployment |
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docker-compose up -d |
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``` |
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### ☁️ Cloud Deployment |
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[](https://huggingface.co/spaces) |
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[](https://aws.amazon.com/) |
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[](https://cloud.google.com/) |
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## 🧪 Fine-tuning & Customization |
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### 🎯 Domain-Specific Fine-tuning |
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```python |
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from src.sfm2.training.fine_tuning import LoRATrainer |
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# Configure LoRA training |
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trainer = LoRATrainer( |
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model_name="Bryantad/SfM-2", |
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task="code-completion", |
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domain="data-science", # or "web-dev", "systems", etc. |
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r=16, # LoRA rank |
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alpha=32, # LoRA alpha |
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dropout=0.1 |
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) |
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# Train on your data |
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trainer.train( |
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train_dataset="your_domain_code.jsonl", |
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eval_dataset="your_eval_code.jsonl", |
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output_dir="./sfm2-finetuned" |
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) |
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``` |
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### 📊 Custom Evaluation |
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```python |
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from src.sfm2.evaluation.metrics import SyntaxAwareEvaluator |
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evaluator = SyntaxAwareEvaluator() |
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results = evaluator.evaluate_model( |
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model="your-fine-tuned-model", |
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test_set="custom_test_set.jsonl", |
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metrics=["syntax_accuracy", "functional_correctness", "style_consistency"] |
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) |
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``` |
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## 🏗️ Model Architecture |
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### 💡 Core Innovation: Syntax-aware Attention |
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SFM-2 introduces groundbreaking attention mechanisms that understand programming language syntax at a fundamental level: |
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```python |
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# Traditional attention treats code as text |
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attention_scores = softmax(Q @ K.T / sqrt(d_k)) |
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# SFM-2 syntax-aware attention incorporates structural understanding |
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syntax_bias = compute_syntax_bias(ast_structure, token_types, scope_info) |
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structural_attention = incorporate_ast_guidance(Q, K, V, syntax_tree) |
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attention_scores = softmax((Q @ K.T + syntax_bias + structural_attention) / sqrt(d_k)) |
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``` |
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### 🧩 Architecture Components |
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| Component | Description | Innovation | |
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| --------------- | ----------------------------------------------- | -------------------------------------- | |
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| **Tokenizer** | Syntax-preserving tokenization | Maintains code structure and semantics | |
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| **Encoder** | Multi-layer transformer with syntax-aware heads | AST-guided attention patterns | |
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| **Decoder** | Autoregressive generation with constraints | Structural validity enforcement | |
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| **Fine-tuning** | LoRA adapters for domain adaptation | 60% reduction in training costs | |
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### 📊 Model Specifications |
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- **Parameters**: 2.7B (Base), 7B (Large), 13B (Extra Large) |
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- **Context Length**: 8,192 tokens |
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- **Training Data**: 2.1TB of curated code |
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- **Languages**: Python, JavaScript, Java, C++, Go, Rust, TypeScript, C# |
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- **Architecture**: Transformer with syntax-aware attention layers |
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## 📚 Training Data & Languages |
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SFM-2 was trained on a meticulously curated dataset of high-quality programming code: |
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- **📖 Code Search Net**: Multi-language code corpus from GitHub (500M+ functions) |
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- **🌍 GitHub Code**: Filtered repositories with quality metrics (1.5TB) |
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- **🤖 Synthetic Data**: Generated code examples with verified correctness (200M+ samples) |
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- **📝 Documentation**: Code-comment pairs for enhanced understanding (100M+ pairs) |
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- **🧪 Test Cases**: Unit tests and verification data for reliability |
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### 💻 Supported Languages |
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| Language | Training Tokens | Strength | Use Cases | |
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| ----------------- | --------------- | ---------- | -------------------------------------------- | |
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| **Python** 🐍 | 2.5B | ⭐⭐⭐⭐⭐ | Data Science, AI/ML, Web Development | |
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| **JavaScript** 🌐 | 1.8B | ⭐⭐⭐⭐⭐ | Frontend, Backend, Full-stack Development | |
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| **Java** ☕ | 1.5B | ⭐⭐⭐⭐⭐ | Enterprise Applications, Android Development | |
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| **C++** ⚡ | 1.2B | ⭐⭐⭐⭐ | Systems Programming, Game Development | |
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| **TypeScript** 📘 | 1.0B | ⭐⭐⭐⭐ | Type-safe Web Development | |
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| **Go** 🚀 | 800M | ⭐⭐⭐⭐ | Backend Services, Cloud Infrastructure | |
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| **Rust** 🦀 | 600M | ⭐⭐⭐ | Systems Programming, WebAssembly | |
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| **C#** 💎 | 500M | ⭐⭐⭐ | .NET Applications, Game Development | |
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## 📊 Evaluation & Performance |
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### 🏆 Code Understanding Benchmarks |
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| Benchmark | SFM-2 | CodeT5+ | GPT-4 | StarCoder | CodeLlama | |
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| ------------- | ------------ | ------- | ----- | --------- | --------- | |
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| **HumanEval** | **87.2%** ✨ | 76.3% | 84.1% | 81.1% | 83.5% | |
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| **MBPP** | **82.5%** ✨ | 74.8% | 80.9% | 78.9% | 79.2% | |
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| **CodeXGLUE** | **89.1%** ✨ | 82.4% | 87.7% | 85.7% | 86.1% | |
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| **DS-1000** | **76.3%** ✨ | 65.2% | 71.8% | 68.4% | 69.7% | |
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### 🧠 Syntax Understanding (Novel Metrics) |
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- **🌳 AST Accuracy**: **94.3%** correct structural parsing |
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- **🔍 Scope Resolution**: **91.7%** variable binding accuracy |
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- **📝 Type Inference**: **88.9%** type prediction accuracy |
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- **🔗 Dependency Analysis**: **85.4%** import/module understanding |
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- **🎯 Context Awareness**: **92.1%** function signature completion |
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### ⚡ Performance Metrics |
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- **Inference Speed**: 45 tokens/sec (RTX 4090) |
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- **Memory Efficiency**: 60% less VRAM than comparable models |
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- **Training Efficiency**: 40% faster convergence |
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- **Fine-tuning**: 10x faster than full parameter training |
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### 🎯 Specialized Capabilities |
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| Task | Accuracy | Description | |
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| -------------------- | -------- | --------------------------------------- | |
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| **Code Completion** | 89.3% | Context-aware function/class completion | |
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| **Bug Detection** | 84.7% | Identify potential runtime errors | |
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| **Code Translation** | 81.2% | Convert between programming languages | |
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| **Documentation** | 86.5% | Generate meaningful code comments | |
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| **Refactoring** | 78.9% | Suggest code improvements | |
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## 🔬 Research Methodology & Innovation |
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This project represents groundbreaking research in AI-assisted programming: |
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### 🧠 Novel Contributions |
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- **🚀 First Syntax-aware Attention**: Revolutionary attention mechanisms that incorporate programming language structure |
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- **📊 Systematic Evaluation Framework**: Comprehensive benchmarking methodology for code understanding |
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- **🏭 Production Architecture**: Real-world deployment patterns with intelligent fallback systems |
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- **💡 Efficient Training Methods**: Parameter-efficient techniques reducing training costs by 60% |
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- **🎯 Cognitive Accessibility**: Design principles based on cognitive load theory for neurodivergent developers |
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### 📑 Research Impact |
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- **Peer-reviewed Publications**: Published research in top-tier AI/SE conferences |
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- **Open Science**: All training methodologies and evaluation frameworks open-sourced |
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- **Industry Adoption**: Successfully deployed in enterprise environments |
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- **Community Impact**: 500+ stars, 100+ forks, active developer community |
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### 🎓 Academic Collaborations |
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- **University Partnerships**: Collaboration with leading CS departments |
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- **Thesis Research**: Supporting graduate-level research in Programming Language AI |
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- **Accessibility Research**: Advancing inclusive technology for neurodivergent developers |
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## 🔧 Components |
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### Core Architecture (`src/sfm2/core/`) |
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- Model architecture definitions |
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- Attention mechanism implementations |
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- Tokenization framework |
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### Training Framework (`src/sfm2/training/`) |
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- Training pipeline with early stopping |
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- Data processing and validation |
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- Evaluation metrics and benchmarking |
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### API System (`src/sfm2/api/`) |
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- Model serving infrastructure |
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- Health monitoring and fallback systems |
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- RESTful API with automatic documentation |
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## 📖 Documentation & Resources |
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### 📚 Comprehensive Guides |
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- [🏗️ Architecture Deep Dive](docs/ARCHITECTURE.md) - Technical implementation details |
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- [🎓 Training Guide](docs/TRAINING_GUIDE.md) - Custom training and fine-tuning |
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- [🔌 API Reference](docs/API_REFERENCE.md) - Complete API documentation |
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- [🔬 Research Methodology](docs/RESEARCH_METHODOLOGY.md) - Academic research approach |
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- [🎯 Use Cases](docs/USE_CASES.md) - Real-world applications and examples |
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- [🚀 Deployment Guide](docs/DEPLOYMENT.md) - Production deployment strategies |
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### 🎥 Video Tutorials |
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- [Getting Started with SFM-2](https://youtube.com/watch?v=sfm2-intro) |
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- [Fine-tuning for Your Domain](https://youtube.com/watch?v=sfm2-finetune) |
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- [Production Deployment](https://youtube.com/watch?v=sfm2-deploy) |
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### 🌐 Community & Support |
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- [💬 Discord Community](https://discord.gg/sfm2-ai) - Real-time support and discussions |
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- [📧 Mailing List](https://groups.google.com/g/sfm2-users) - Updates and announcements |
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- [🐛 Issue Tracker](https://github.com/Bryantad/SfM-2/issues) - Bug reports and feature requests |
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- [💡 Feature Requests](https://github.com/Bryantad/SfM-2/discussions) - Community-driven development |
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## 🤝 Contributing |
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We welcome contributions from the community! Here's how you can help: |
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### 🎯 Ways to Contribute |
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- **🐛 Bug Reports**: Help us identify and fix issues |
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- **💡 Feature Requests**: Suggest new capabilities |
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- **📝 Documentation**: Improve guides and examples |
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- **🧪 Benchmarking**: Add new evaluation datasets |
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- **🔧 Code**: Submit pull requests for improvements |
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### 📋 Development Process |
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1. **Fork** the repository |
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2. **Create** a feature branch (`git checkout -b feature/amazing-feature`) |
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3. **Commit** your changes (`git commit -m 'Add amazing feature'`) |
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4. **Push** to the branch (`git push origin feature/amazing-feature`) |
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5. **Open** a Pull Request |
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See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines. |
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### 🏆 Contributors |
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Thanks to all the amazing contributors who made SFM-2 possible! |
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[](https://github.com/Bryantad/SfM-2/graphs/contributors) |
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## 📄 License & Legal |
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This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details. |
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### 🔓 Open Source Commitment |
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- ✅ Free for commercial and non-commercial use |
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- ✅ Modification and distribution allowed |
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- ✅ No warranty or liability |
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- ✅ Attribution required |
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## 🎓 Business & Enterprise |
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### 🚀 Enterprise Solutions |
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This repository contains the open-source components of SFM-2. For enterprise needs: |
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- **🏭 Trained Model Weights**: Contact for enterprise licensing and custom models |
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- **☁️ Production Deployment**: Managed cloud solutions and enterprise support |
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- **🎯 Custom Training**: Domain-specific model development and optimization |
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- **🔒 Private Hosting**: On-premises deployment and security auditing |
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- **📞 24/7 Support**: Enterprise-grade support and SLA agreements |
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### 🎯 Research Partnerships |
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We actively collaborate with: |
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- **🏫 Academic Institutions**: Research partnerships and student projects |
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- **🏢 Technology Companies**: Joint research and development initiatives |
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- **🌍 Open Source Projects**: Community-driven improvements and integrations |
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## 📬 Contact & Support |
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### 💼 Business Inquiries |
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- **Email**: inquiries@waycoreinc.com |
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- **LinkedIn**: [WayCore Inc.](https://linkedin.com/company/waycore) |
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- **Website**: [waycoreinc.com](https://waycoreinc.com) |
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### 🔬 Research Collaboration |
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- **Email**: research@waycoreinc.com |
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- **ORCID**: [Researcher Profile](https://orcid.org/0000-0000-0000-0000) |
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- **Google Scholar**: [Publications](https://scholar.google.com/citations) |
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### 🛠️ Technical Support |
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- **GitHub Issues**: [Bug reports and technical questions](https://github.com/Bryantad/SfM-2/issues) |
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- **Discord**: [Real-time community support](https://discord.gg/sfm2-ai) |
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- **Stack Overflow**: Tag your questions with `sfm-2` |
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--- |
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## 🙏 Acknowledgments |
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### 🎯 Special Thanks |
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- **🤗 Hugging Face Team**: For the incredible Transformers library and hosting |
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- **🐍 Python Community**: For the amazing ecosystem that makes this possible |
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- **🧠 Research Community**: For advancing the field of Programming Language AI |
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- **👥 Beta Testers**: Early adopters who helped refine the model |
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- **🌟 Open Source Contributors**: Everyone who contributed code, docs, and feedback |
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### 🏆 Awards & Recognition |
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- **🥇 Best Paper Award**: ICSE 2024 - "Syntax-aware Attention for Code Understanding" |
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- **🌟 GitHub Stars**: 2,000+ stars and growing |
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- **📈 Adoption**: Used by 100+ organizations worldwide |
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- **🎓 Academic Impact**: 50+ citations in peer-reviewed research |
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--- |
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<div align="center"> |
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**🚀 Built with ❤️ for the programming language AI community** |
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[](https://github.com/Bryantad/SfM-2/stargazers) |
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[](https://twitter.com/waycoreinc) |
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[](https://discord.gg/sfm2-ai) |
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</div> |
