---

library_name: gguf

base_model:

• arun11karthik/cellsense-fim-3b

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-3b-GGUF

GGUF quantisations of arun11karthik/cellsense-fim-3b, a fill-in-the-middle (FIM) code-completion model.

Available files

| File | Type | Notes |

|------|------|-------|

| cellsense-fim-3b-BF16.gguf | BF16 | Full bfloat16 precision — highest quality |

| cellsense-fim-3b-Q8_0.gguf | Q8_0 | Near-lossless 8-bit quantisation |

| cellsense-fim-3b-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-3b-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 | 2.2 GB | Good quality / size trade-off | ollama run hf.co/arun11karthik/cellsense-fim-3b-GGUF:Q5_K_M |

| Q8_0 | 3.3 GB | Near-lossless 8-bit quantization | ollama run hf.co/arun11karthik/cellsense-fim-3b-GGUF:Q8_0 |

| BF16 | 6.2 GB | Full bfloat16 precision — highest quality (recommended) | ollama run hf.co/arun11karthik/cellsense-fim-3b-GGUF:BF16 |

1. Install Ollama and pull the model

Install Ollama, then pull a quantization (this also registers the model so

CellSense can use it):

ollama pull hf.co/arun11karthik/cellsense-fim-3b-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-3b-GGUF:Q5_K_M — is what you'll enter into

CellSense below.

2. Install the CellSense JupyterLab plugin

pip install jupyterlab-cellsense
jupyter lab

See the CellSense repository 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-3b-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:

curl http://localhost:11434/api/generate -d '{
  "model": "hf.co/arun11karthik/cellsense-fim-3b-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 }
}'