初始化项目，由ModelHub XC社区提供模型

Model: arun11karthik/cellsense-fim-3b-GGUF
Source: Original Platform

README.md

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+---
+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`](https://huggingface.co/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`**](https://huggingface.co/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](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-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
+
+```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-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:
+
+```bash
+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 }
+}'
+```
+