File	Type	Notes
`cellsense-fim-7b-BF16.gguf`	BF16	Full bfloat16 precision — highest quality
`cellsense-fim-7b-Q8_0.gguf`	Q8_0	Near-lossless 8-bit quantisation
`cellsense-fim-7b-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-7b-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`	5.4 GB	Good quality / size trade-off	`ollama run hf.co/arun11karthik/cellsense-fim-7b-GGUF:Q5_K_M`
`Q8_0`	8.1 GB	Near-lossless 8-bit quantization (recommended)	`ollama run hf.co/arun11karthik/cellsense-fim-7b-GGUF:Q8_0`
`BF16`	15.2 GB	Full bfloat16 precision — highest quality	`ollama run hf.co/arun11karthik/cellsense-fim-7b-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-7b-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-7b-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 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-7b-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-7b-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 }
}'