Update README.md

README.md

@@ -1,3 +1,169 @@
----
-license: mit
----
+# 🚀 MiniCoderX: A Lightweight Transformer for Code Generation
+
+**MiniCoderX** is a structure-aware, transformer-based small language model (SLM) for code generation. It blends modern architectural techniques with efficient deployment using tools like **LangChain** and **Ollama**, making it ideal for rapid local experimentation.
+
+---
+
+## ✨ Features
+
+- 🧠 Transformer-based encoder-decoder (TinyCodeT5 / DistilGPT2)
+- 🌲 AST/CFG-aware encoding for code structure understanding
+- 💾 Syntax-constrained decoding using grammar rules and trees
+- 🔁 Multi-task heads: generation, summarization, translation, bug fixing
+- ⚙️ LangChain + Ollama integration for fast local deployment
+- 🧪 Evaluated on HumanEval, CodeXGLUE, MBPP
+
+---
+
+## 🏗️ Model Architecture
+
+| Component       | Description                                               |
+|----------------|-----------------------------------------------------------|
+| Base           | Tiny encoder-decoder (MiniLM, DistilGPT2, TinyCodeT5)     |
+| Structure-aware | AST and Control Flow Graph embeddings + positional masks |
+| Heads          | Multi-task heads for flexible downstream use              |
+| Decoder        | Syntax-aware beam search (grammar constraints)            |
+| Tokenizer      | BPE or SentencePiece trained on code + comments           |
+
+---
+
+## 🔧 Architectural Additions (SOTA Techniques)
+
+### 🌲 AST/CFG Embeddings
+Enhances understanding of code structure by:
+- Adding AST node/edge embeddings to token inputs
+- Including path embeddings between syntactic elements
+- Graph-aware position encoding
+
+Inspired by: **StructCoder**, **AST-T5**, **Code4Struct**
+
+### 💾 Syntax-Constrained Decoding
+Improves generation accuracy and reduces invalid code by:
+- Restricting token outputs using grammar constraints (BNF/PEG)
+- Custom decoding logic (e.g., Tree traversal)
+- Dynamic decoding masks based on token state
+
+Inspired by: **TreeGen**, **Code4Struct**
+
+### 🔁 Multi-Task Learning Heads
+Supports multiple tasks:
+- Code generation (NL → Code)
+- Summarization (Code → NL)
+- Translation (Java ⇄ Python)
+- Code repair and completion
+
+Inspired by: **CodeT5+**, **CoTexT**
+
+---
+
+## ⚡ LangChain + Ollama Integration
+
+### 💡 Why?
+To enable:
+- 🧪 Local testing and chaining of models via **LangChain**
+- 🦮 Fast prototyping with **Ollama** for custom transformer backends
+- 🔄 Easy switch between small local models and larger remote APIs
+
+### 🔌 Integration Plan
+```python
+from langchain.llms import Ollama
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# Load MiniCoderX with Ollama
+llm = Ollama(model="minicoderx")  # Local model via Ollama
+
+# Define code generation prompt
+prompt = PromptTemplate(
+    input_variables=["instruction"],
+    template="Generate Python code for the task: {instruction}",
+)
+
+chain = LLMChain(llm=llm, prompt=prompt)
+result = chain.run("Sort a list of integers using quicksort")
+
+print(result)
+```
+
+> ✅ Ollama will be used to serve your fine-tuned SLM locally  
+> ✅ LangChain will wrap it with prompts, chains, and memory features for interactivity
+
+---
+
+## 📦 Datasets
+
+| Dataset        | Use                        |
+|----------------|----------------------------|
+| The Stack (subset) | Pretraining corpus     |
+| CodeSearchNet  | Summarization, Search      |
+| HumanEval      | Code generation benchmark  |
+| MBPP           | Python programming prompts |
+| Bugs2Fix       | Code repair                |
+| Java-Python    | Cross-language translation |
+
+---
+
+## 🔬 Training Objectives
+
+- ✅ Span Masking (CodeT5-style)
+- ✅ Contrastive pretraining
+- ✅ Instruction tuning (natural prompt formatting)
+- ✅ Auto-regressive generation
+
+---
+
+## 📊 Evaluation Benchmarks
+
+| Benchmark  | Metric            |
+|------------|-------------------|
+| HumanEval  | Pass@1, BLEU      |
+| MBPP       | Accuracy          |
+| CodeXGLUE  | CodeBLEU, EM      |
+| Unit Tests | Pass Rate         |
+
+---
+
+## 🧪 Project Roadmap
+
+### ✅ Phase 1: MVP Model
+- Train TinyCodeT5 model with span masking
+- Evaluate on MBPP and HumanEval-lite
+- Serve via Ollama + LangChain prompt chain
+
+### 🔁 Phase 2: Structural Learning
+- Add AST/CFG encodings
+- Introduce grammar-constrained decoding
+- Multi-task training (gen, sum, repair)
+
+### 📦 Phase 3: Optimization & Packaging
+- Distill from larger model (e.g., StarCoder)
+- Add reinforcement fine-tuning via test cases
+- Export to Hugging Face + Ollama integration
+
+---
+
+## 🛠️ Tools & Frameworks
+
+- [Hugging Face Transformers](https://github.com/huggingface/transformers)
+- [LangChain](https://github.com/langchain-ai/langchain)
+- [Ollama](https://ollama.com/)
+- SentencePiece / BPE
+- NetworkX for AST/CFG parsing
+
+---
+
+## 🤝 Contributing
+
+Want to help with grammar decoders, AST integration, or evaluation? PRs welcome!
+
+---
+
+## 📜 License
+
+MIT License. Built for research and open experimentation.
+
+---
+
+## 📧 Contact
+
+Drop an issue or discussion on GitHub!