docs: add Hugging Face Model Card with tags and usage

README.md

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+---
+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)
+
+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}
+}
+```