File size: 3,643 Bytes
e9f91af 7eb899e e9f91af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | ---
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}
}
```
|