---
library_name: gguf
base_model:
- arun11karthik/cellsense-fim-1.5b
pipeline_tag: text-generation
tags:
- cellsense
- fim
- code
- gguf
---

<p align="center">
  <img src="icon.png" alt="CellSense" width="180" height="180" />
</p>

# arun11karthik/cellsense-fim-1.5b-GGUF

GGUF quantisations of [`arun11karthik/cellsense-fim-1.5b`](https://huggingface.co/arun11karthik/cellsense-fim-1.5b), a fill-in-the-middle (FIM) code-completion model.

## Available files

| File | Type | Notes |
|------|------|-------|
| `cellsense-fim-1.5b-BF16.gguf`   | BF16   | Full bfloat16 precision — highest quality |
| `cellsense-fim-1.5b-Q8_0.gguf`   | Q8_0   | Near-lossless 8-bit quantisation |
| `cellsense-fim-1.5b-Q5_K_M.gguf` | Q5_K_M | Good quality / size trade-off |

## Usage

For fully local, no-GPU-required inference, GGUF builds are published at
[**`arun11karthik/cellsense-fim-1.5b-GGUF`**](https://huggingface.co/arun11karthik/cellsense-fim-1.5b-GGUF).
Ollama can pull and run these directly from the Hugging Face Hub — no manual download or `Modelfile`
required. This is the recommended path for running CellSense entirely on your own machine: with Ollama,
**no code or context ever leaves your computer.**

### Available quantizations

| Quantization | Size (approx.) | Notes | Pull with |
|--------------|:--------------:|-------|-----------|
| `Q5_K_M` | 1.13 GB | Good quality / size trade-off | `ollama run hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q5_K_M` |
| `Q8_0`   | 1.65 GB | Near-lossless 8-bit quantization | `ollama run hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q8_0` |
| `BF16`   | 3.09 GB | Full bfloat16 precision — highest quality (recommended) | `ollama run hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:BF16` |

### 1. Install Ollama and pull the model

Install [Ollama](https://ollama.com), then pull a quantization (this also registers the model so
CellSense can use it):

```bash
ollama pull hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q5_K_M
```

By default Ollama serves its API at `http://localhost:11434`. The model name as it appears in
`ollama list` — `hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q5_K_M` — is what you'll enter into
CellSense below.

### 2. Install the CellSense JupyterLab plugin

```bash
pip install jupyterlab-cellsense
jupyter lab
```

See the [CellSense repository](https://github.com/arunkarthik11/CellSense) for full installation
options.

### 3. Point CellSense at your local Ollama model

Open the CellSense panel from the left sidebar in JupyterLab, go to **Basic Settings**, and configure
the **Ollama** provider:

| Setting | Value |
|---------|-------|
| **Provider** | `Ollama` |
| **Base URL** | `http://localhost:11434` |
| **Model Family** | `cellsense` |
| **Model** | `hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q5_K_M` (must match the tag in `ollama list`) |

> ✅ **Set Model Family to `cellsense`.** CellSense now ships first-class support for the CellSense-FIM
> models, so the plugin builds prompts in exactly the repository-, import-, and task-aware FIM format
> these models were trained on — no extra configuration needed.

Click **Save & Apply**, then start typing in a notebook cell — ghost-text completions from your local
model appear inline. Press **Tab** to accept.

### Raw API check (optional)

To confirm Ollama is serving the model with the correct FIM format before wiring up CellSense, query it
directly:

```bash
curl http://localhost:11434/api/generate -d '{
  "model": "hf.co/arun11karthik/cellsense-fim-1.5b-GGUF:Q5_K_M",
  "prompt": "<|fim_prefix|>import pandas as pd\ndf = pd.read_csv(\"data.csv\")\n<|fim_suffix|>\ndf.head()\n<|fim_middle|>",
  "stream": false,
  "options": { "temperature": 0.0, "num_predict": 128 }
}'
```