Instructions to use almax000/cellsentry-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use almax000/cellsentry-model with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("almax000/cellsentry-model")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use almax000/cellsentry-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="almax000/cellsentry-model",
	filename="cellsentry-1.5b-v3-q4km.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use almax000/cellsentry-model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf almax000/cellsentry-model
# Run inference directly in the terminal:
llama-cli -hf almax000/cellsentry-model

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf almax000/cellsentry-model
# Run inference directly in the terminal:
llama-cli -hf almax000/cellsentry-model

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf almax000/cellsentry-model
# Run inference directly in the terminal:
./llama-cli -hf almax000/cellsentry-model

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf almax000/cellsentry-model
# Run inference directly in the terminal:
./build/bin/llama-cli -hf almax000/cellsentry-model

Use Docker

docker model run hf.co/almax000/cellsentry-model

LM Studio
Jan

vLLM

How to use almax000/cellsentry-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "almax000/cellsentry-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "almax000/cellsentry-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/almax000/cellsentry-model

Ollama
How to use almax000/cellsentry-model with Ollama:
```
ollama run hf.co/almax000/cellsentry-model
```

Unsloth Studio new

How to use almax000/cellsentry-model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for almax000/cellsentry-model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for almax000/cellsentry-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for almax000/cellsentry-model to start chatting

Pi new

How to use almax000/cellsentry-model with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "almax000/cellsentry-model"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "almax000/cellsentry-model"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use almax000/cellsentry-model with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "almax000/cellsentry-model"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default almax000/cellsentry-model

Run Hermes

hermes

MLX LM

How to use almax000/cellsentry-model with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "almax000/cellsentry-model"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "almax000/cellsentry-model"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "almax000/cellsentry-model",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use almax000/cellsentry-model with Docker Model Runner:
```
docker model run hf.co/almax000/cellsentry-model
```

Lemonade

How to use almax000/cellsentry-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull almax000/cellsentry-model

Run and chat with the model

lemonade run user.cellsentry-model-{{QUANT_TAG}}

List all available models

lemonade list

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
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, an open-source desktop app for spreadsheet auditing. The app downloads the model automatically on first launch.

Manual Download

# 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)

CellSentry App — Desktop app that uses this model
CellSentry Website — Project homepage

Downloads last month: 5

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for almax000/cellsentry-model

Base model

Qwen/Qwen2.5-1.5B

Adapter

(505)

this model

almax000
/

cellsentry-model