| --- |
| language: |
| - en |
| - code |
| license: apache-2.0 |
| tags: |
| - code-generation |
| - code-documentation |
| - non-autoregressive |
| - stanford-ncm |
| - vq-vae |
| - mcp |
| - codesearchnet |
| datasets: |
| - code_search_net |
| pipeline_tag: text-generation |
| --- |
| |
| # NCM Code Documentation (Next-Concept Modeling) |
|
|
| <p align="center"> |
| <img src="assets/ncm_pipeline.gif" alt="NCM MCP Pipeline" width="780" /> |
| </p> |
|
|
|
|
| Fine-tuned checkpoint of Stanford's **Next-Concept Modeling (NCM)** architecture, adapted for ultra-fast, local code documentation and technical function summarization. |
|
|
| - **GitHub Repository**: [artapaher-max/ncm-code-doc](https://github.com/artapaher-max/ncm-code-doc) |
| - **Base Architecture**: Stanford University / Hazy Research ([HazyResearch/ncm](https://github.com/HazyResearch/ncm)) |
| - **Primary Integration**: Model Context Protocol (MCP) server for local IDE agents (Antigravity IDE, Claude Desktop, Cursor). |
|
|
| --- |
|
|
| ## Model Architecture & Details |
|
|
| NCM operates in a **non-autoregressive (NAR)** latent space through a 9-level hierarchical Vector-Quantized Variational Autoencoder (VQ-VAE). |
|
|
| - **Non-Autoregressive Generation**: Unlike standard autoregressive LLMs (which decode one token at a time), NCM decodes conceptual representations in parallel across 9 quantization depth levels. |
| - **Inference Latency**: ~50–100 ms per block on consumer desktop GPUs (e.g., NVIDIA RTX 2060 6GB). |
| - **Target Context Length**: Bounded to $\le 48$ tokens. Optimized specifically for single-sentence docstrings, function signatures, and concise technical summaries. |
| - **Prefix Window**: Up to 512 raw tokens of source code. |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| This model is intended to act as a **lightweight, local writing assistant** offloading routine documentation tasks from large frontier LLMs (Claude 3.5 Sonnet, GPT-4, Gemini): |
|
|
| 1. **Token Cost & Latency Reduction**: Avoid spending frontier model tokens on repetitive single-sentence function descriptions. |
| 2. **Local MCP Server**: Run locally alongside coding assistants via stdio MCP. |
|
|
| --- |
|
|
| ## Quickstart & Usage |
|
|
| ### 1. Download Model Checkpoints |
|
|
| Using Python: |
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="r2d2aparicio/ncm-code-doc", |
| local_dir="artifacts/ncm_code_doc" |
| ) |
| ``` |
|
|
| Files downloaded: |
| - `generator.pt` (~354 MB): NextConceptTransformer weights. |
| - `tokenizer.pt` (~299 MB): Hierarchical VQ-VAE tokenizer weights with code-adapted decoder. |
|
|
| ### 2. Run with MCP Server |
|
|
| To use this model as a tool in Claude Desktop or Antigravity IDE, clone the official MCP server: |
|
|
| ```bash |
| git clone https://github.com/artapaher-max/ncm-code-doc.git |
| cd ncm-code-doc |
| pip install -e . |
| ``` |
|
|
| And configure `mcpServers`: |
| ```json |
| { |
| "mcpServers": { |
| "ncm-fast-writer": { |
| "command": "/path/to/ncm-code-doc/.venv/bin/python", |
| "args": ["-u", "/path/to/ncm-code-doc/ncm_doc_server.py"], |
| "env": { "PYTHONUNBUFFERED": "1" } |
| } |
| } |
| } |
| ``` |
|
|
| --- |
|
|
| ## Training Data & Hardware |
|
|
| - **Dataset**: 5,000 code-docstring pairs sampled from `code_search_net` across Python, JavaScript, and Web repositories. |
| - **Fine-Tuning Hardware**: Single NVIDIA GeForce RTX 2060 (6 GB VRAM GDDR6). |
| - **Memory Consumption**: ~2.9 GB VRAM during training (FP16 mixed precision, gradient accumulation, batch size 2). |
| - **Inference Memory**: ~2.5 GB VRAM. |
|
|
| --- |
|
|
| ## Citation & Acknowledgments |
|
|
| This model is fine-tuned from the checkpoint provided by Stanford Hazy Research: |
|
|
| ```bibtex |
| @article{ncm2024, |
| title={Next-Concept Modeling: Scalable Non-Autoregressive Generation via Hierarchical Latent Space}, |
| author={Hazy Research, Stanford University}, |
| journal={arXiv preprint}, |
| year={2024} |
| } |
| ``` |
|
|
