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README.md

@@ -1,3 +1,112 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen2.5-1.5B
+tags:
+- cellsentry
+- excel
+- spreadsheet
+- formula-audit
+- pii-detection
+- data-extraction
+- gguf
+- mlx
+- lora
+- qwen2.5
+pipeline_tag: text-generation
+---
+
+# CellSentry Model — Multi-Task Spreadsheet AI
+
+A fine-tuned 1.5B parameter model for spreadsheet intelligence tasks. Built on Qwen2.5-1.5B with LoRA, this model handles three distinct tasks through prompt routing:
+
+- **Formula Audit** — Verify or dismiss rule engine findings in Excel formulas
+- **PII Detection** — Identify sensitive data (SSN, phone, email, national IDs) in cell values
+- **Data Extraction** — Extract structured fields (invoice number, date, vendor, totals) from spreadsheets
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base model | [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) |
+| Fine-tuning | LoRA (rank 16, alpha 32) |
+| Training | 4000 iterations, batch_size=2, lr=3e-5, AdamW |
+| Quantization | 4-bit, group_size=32 (Q4_K_M for GGUF) |
+| Context length | 1024 tokens |
+| License | MIT |
+
+## Available Formats
+
+| Format | File | Size | Platform |
+|--------|------|------|----------|
+| **GGUF** (Q4_K_M) | `cellsentry-1.5b-v3-q4km.gguf` | ~940 MB | Windows (llama.cpp) |
+| **MLX** (4-bit g32) | `cellsentry-1.5b-v3-4bit-g32/` | ~920 MB | macOS (MLX) |
+
+> Currently only the GGUF format is uploaded. MLX format coming soon.
+
+## Usage
+
+This model is designed to be used with [CellSentry](https://github.com/almax000/cellsentry), an open-source desktop app for spreadsheet auditing. The app downloads the model automatically on first launch.
+
+### Manual Download
+
+```bash
+# Install Hugging Face CLI
+pip install huggingface-hub
+
+# Download GGUF model
+huggingface-cli download almax000/cellsentry-model cellsentry-1.5b-v3-q4km.gguf --local-dir ./models
+```
+
+### Prompt Format
+
+The model uses Qwen2.5 chat template with task-specific system prompts:
+
+**Formula Audit:**
+```
+<|im_start|>system
+You are a spreadsheet formula auditor...<|im_end|>
+<|im_start|>user
+{rule engine finding + cell context}<|im_end|>
+<|im_start|>assistant
+```
+
+**PII Detection:**
+```
+<|im_start|>system
+You are a PII detection specialist...<|im_end|>
+<|im_start|>user
+{cell values to scan}<|im_end|>
+<|im_start|>assistant
+```
+
+**Data Extraction:**
+```
+<|im_start|>system
+You are a document data extractor...<|im_end|>
+<|im_start|>user
+{spreadsheet content + template}<|im_end|>
+<|im_start|>assistant
+```
+
+## Training
+
+- **Method**: LoRA fine-tuning with multi-task data
+- **Data**: Synthetic + real-world spreadsheet samples across all three tasks
+- **Fusion**: LoRA weights fused into base model, then quantized (dequantize → fuse → re-quantize with group_size=32)
+- **Key lesson**: group_size=64 loses fine-tuning quality; group_size=32 is the minimum viable floor for 1.5B models
+
+## Limitations
+
+- Optimized for structured spreadsheet content, not general text
+- 1024 token context — large spreadsheets need chunking
+- PII patterns trained primarily on US and Chinese formats
+- Extraction templates cover 5 document types (invoice, receipt, PO, expense, payroll)
+
+## Related
+
+- [CellSentry App](https://github.com/almax000/cellsentry) — Desktop app that uses this model
+- [CellSentry Website](https://cellsentry.pro) — Project homepage