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Model: arun11karthik/cellsense-fim-0.5b Source: Original Platform
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen2.5-Coder-0.5B
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language:
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- code
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tags:
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- cellsense
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- fim
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- fill-in-the-middle
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- code-completion
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- jupyter
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- notebook
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- code
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- qwen2
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model-index:
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- name: cellsense-fim-0.5b
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results:
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- task:
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type: text-generation
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name: Fill-in-the-Middle Code Completion
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dataset:
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name: CellSense Jupyter FIM (private)
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type: cellsense-fim-large
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split: test
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metrics:
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- type: edit_similarity
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value: 0.641
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name: Edit Similarity
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- type: bleu
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value: 43.09
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name: BLEU
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- type: codebleu
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value: 0.346
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name: CodeBLEU
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- type: token_accuracy
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value: 0.868
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name: Token Accuracy
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- type: bits_per_byte
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value: 0.254
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name: Bits per Byte
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---
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<p align="center">
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<img src="icon.png" alt="CellSense" width="180" height="180" />
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</p>
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# CellSense-FIM 0.5B
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<!-- TODO: replace OWNER/REPO with your GitHub repo and PACKAGE with your PyPI package name -->
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[](https://huggingface.co/spaces/arun11karthik/cellsense-fim)
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[](https://github.com/arunkarthik11/cellsense)
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[](https://pypi.org/project/jupyterlab-cellsense/)
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**CellSense-FIM 0.5B** is the smallest member of the CellSense-FIM model family — a set of
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**long-context, fill-in-the-middle (FIM) code-completion models built specifically for Jupyter
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notebooks.** It is fine-tuned from [`Qwen/Qwen2.5-Coder-0.5B`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B)
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on the (private) **CellSense FIM** dataset and supports a **32K-token context window**.
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Unlike general code models that treat a notebook as a flat file, CellSense-FIM models are trained
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on a more detailed context, that actually matters when you complete a cell:
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- **🗂️ Repository-aware** — the model is trained with surrounding files from the same repository in
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context, so completions respect helpers, constants, and conventions defined elsewhere in the project.
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- **🔗 Local-import-aware** — when your notebook imports from a sibling module, the relevant source and signatures are
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pulled into context, so the model completes calls to *your* code with the right signatures, not a
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plausible guess.
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- **🎯 Task-aware** — the files you have been reading and editing are almost always the most relevant to
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what you are working on right now. The context conditions on this context, so completions reflect where
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your attention has actually been — not just what happens to be open in the active tab.
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The models are best paired with the **CellSense Jupyter Lab Plugin**, which assembles repository, local-import,
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and task context into the exact format the model was trained on — so the model consumes it natively with
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no prompt engineering on your part.
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## Model family
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| Model | Base | Params | Context |
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|-------|------|:------:|:-------:|
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| **CellSense-FIM 0.5B** (this model) | Qwen2.5-Coder-0.5B | 0.5B | 32K |
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| CellSense-FIM 1.5B | Qwen2.5-Coder-1.5B | 1.5B | 32K |
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| CellSense-FIM 3B | Qwen2.5-Coder-3B | 3B | 32K |
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| CellSense-FIM 7B | Qwen2.5-Coder-7B | 7B | 32K |
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## Evaluation
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Evaluated on the held-out `test` split of the CellSense FIM dataset. CellSense-FIM is compared against its base model
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(`Qwen2.5-Coder-0.5B`) and a same-size general model (`Qwen3-0.6B`).
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Fine-tuning on notebook-native FIM context yields large gains on the metrics that track *real completion
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quality* — **edit similarity (0.11 → 0.64)** and **BLEU (4.7 → 43.1)** — while keeping token accuracy
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strong. At 0.5B, CodeBLEU stays close to the base model; the larger family members improve it outright.
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### Likelihood metric — Bits per Byte (lower is better)
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| Model | Bits per Byte ↓ |
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|-------|:---------------:|
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| Qwen2.5-Coder-0.5B (base) | 0.305 |
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| Qwen3-0.6B (base) | 1.082 |
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| **CellSense-FIM 0.5B (ours)** | **0.254** |
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## Usage
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### Prompt format (FIM)
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The model uses the Qwen2.5-Coder FIM sentinels. For a single-file completion:
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```
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<|fim_prefix|>{code before the cursor}<|fim_suffix|>{code after the cursor}<|fim_middle|>
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```
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For repository / local-import-aware completion, prepend the relevant files before the FIM block:
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```
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<|repo_name|>{repo}<|file_sep|>{path/to/helper.py}
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{contents of helper.py}
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<|file_sep|>{path/to/notebook_cell}
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<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>
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```
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> 💡 In practice you don't assemble this by hand — the **CellSense JupyterLab plugin** builds the
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> repository-, import-, and task-aware context and emits exactly this format (see
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> [Serving with vLLM + CellSense](#serving-with-vllm--cellsense) below).
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "arun11karthik/cellsense-fim-0.5b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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prefix = "import pandas as pd\ndf = pd.read_csv('data.csv')\n"
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suffix = "\ndf.head()\n"
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prompt = f"<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Serving with vLLM + CellSense
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The intended way to use this model is to serve it with vLLM and point the
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[**CellSense JupyterLab plugin**](https://github.com/arunkarthik11/CellSense) at the server over its
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OpenAI-compatible API. CellSense handles all of the repository-, import-, and task-aware context
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assembly and the FIM prompt formatting for you.
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### 1. Serve the model with vLLM
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vLLM exposes an OpenAI-compatible endpoint, which is exactly what CellSense's `openai_compatible`
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provider expects:
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```bash
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pip install vllm
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vllm serve arun11karthik/cellsense-fim-0.5b \
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--served-model-name cellsense-fim-0.5b \
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--max-model-len 32768 \
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--port 8000
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```
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This serves the API at `http://localhost:8000/v1`.
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### 2. Point CellSense at the vLLM server
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In the CellSense settings panel (left sidebar in JupyterLab), open **Basic Settings** and configure the
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**OpenAI Compatible** provider:
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| Setting | Value |
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|---------|-------|
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| **Provider** | `OpenAI Compatible` |
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| **Base URL** | `http://localhost:8000/v1` |
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| **API Key** | any non-empty string (vLLM ignores it, e.g. `EMPTY`) |
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| **Model** | `cellsense-fim-0.5b` (must match `--served-model-name`) |
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| **Model Family** | `qwen2.5-coder` |
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Click **Save & Apply**, then start typing in a notebook cell — ghost-text completions from your
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local model appear inline. Press **Tab** to accept.
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> 🌐 The same setup works for a **remote** vLLM server: serve the model on your GPU box, expose port
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> `8000`, and set CellSense's **Base URL** to `http://<host>:8000/v1`.
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### Raw API check (optional)
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To confirm the endpoint works before wiring up CellSense, query it directly with the FIM prompt:
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```bash
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "cellsense-fim-0.5b",
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"prompt": "<|fim_prefix|>import pandas as pd\ndf = pd.read_csv(\"data.csv\")\n<|fim_suffix|>\ndf.head()\n<|fim_middle|>",
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"max_tokens": 128,
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"temperature": 0.0
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}'
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```
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## Ollama
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For fully local, no-GPU-required inference, GGUF builds are published at
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[**`arun11karthik/cellsense-fim-0.5b-GGUF`**](https://huggingface.co/arun11karthik/cellsense-fim-0.5b-GGUF).
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Ollama can pull and run these directly from the Hugging Face Hub — no manual download or `Modelfile`
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required. This is the recommended path for running CellSense entirely on your own machine: with Ollama,
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**no code or context ever leaves your computer.**
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### Available quantizations
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| Quantization | Size (approx.) | Notes | Pull with |
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|--------------|:--------------:|-------|-----------|
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| `Q5_K_M` | ~420 MB | Good quality / size trade-off | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M` |
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| `Q8_0` | ~531 MB | Near-lossless 8-bit quantization | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q8_0` |
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| `BF16` | ~994 MB | Full bfloat16 precision — highest quality (recommended) | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:BF16` |
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### 1. Install Ollama and pull the model
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Install [Ollama](https://ollama.com), then pull a quantization (this also registers the model so
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CellSense can use it):
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```bash
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ollama pull hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M
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```
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By default Ollama serves its API at `http://localhost:11434`. The model name as it appears in
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`ollama list` — `hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M` — is what you'll enter into
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CellSense below.
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### 2. Install the CellSense JupyterLab plugin
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```bash
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pip install jupyterlab-cellsense
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jupyter lab
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```
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See the [CellSense repository](https://github.com/arunkarthik11/CellSense) for full installation
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options.
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### 3. Point CellSense at your local Ollama model
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Open the CellSense panel from the left sidebar in JupyterLab, go to **Basic Settings**, and configure
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the **Ollama** provider:
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| Setting | Value |
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|---------|-------|
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| **Provider** | `Ollama` |
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| **Base URL** | `http://localhost:11434` |
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| **Model Family** | `cellsense` |
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| **Model** | `hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M` (must match the tag in `ollama list`) |
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> ✅ **Set Model Family to `cellsense`.** CellSense now ships first-class support for the CellSense-FIM
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> models, so the plugin builds prompts in exactly the repository-, import-, and task-aware FIM format
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> these models were trained on — no extra configuration needed.
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Click **Save & Apply**, then start typing in a notebook cell — ghost-text completions from your local
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model appear inline. Press **Tab** to accept.
|
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### Raw API check (optional)
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|
|
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To confirm Ollama is serving the model with the correct FIM format before wiring up CellSense, query it
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directly:
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|
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```bash
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curl http://localhost:11434/api/generate -d '{
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"model": "hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M",
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"prompt": "<|fim_prefix|>import pandas as pd\ndf = pd.read_csv(\"data.csv\")\n<|fim_suffix|>\ndf.head()\n<|fim_middle|>",
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"stream": false,
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"options": { "temperature": 0.0, "num_predict": 128 }
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}'
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```
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## Training
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- **Base model:** `Qwen/Qwen2.5-Coder-0.5B`
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- **Dataset:** the private **CellSense FIM** dataset — repository-, local-import-, and task-aware FIM examples mined from Jupyter notebooks
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- **Objective:** fill-in-the-middle (FIM) code completion
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- **Context length:** 32,768 tokens
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- **Checkpoint:** best-validation checkpoint
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## Training data & privacy
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CellSense-FIM was fine-tuned on a private corpus of fill-in-the-middle examples mined from Jupyter
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|
notebooks. The dataset is **kept private as a precaution**: PII masking was applied across the corpus
|
||||||
|
and **more rigororusly verified on the sampled subset used for training**, but full masking across the entire
|
||||||
|
source corpus has only been preliminarily checked and is **not guaranteed**.
|
||||||
|
|
||||||
|
As with any model trained on scraped code, this model may reproduce content from its training data,
|
||||||
|
including any imperfectly masked sensitive strings.
|
||||||
|
If you observe any leakage, please report it via the project's issue tracker (see the GitHub link above).
|
||||||
|
|
||||||
|
## License
|
||||||
|
|
||||||
|
The finetuned weights are released under the GNU 3.0 License, whereas the base model was released under the Apache 2.0 license.
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@misc{cellsense-fim,
|
||||||
|
title = {CellSense-FIM: Notebook-Native Fill-in-the-Middle Code Completion},
|
||||||
|
author = {Arun Karthik},
|
||||||
|
year = {2026},
|
||||||
|
howpublished = {\url{https://huggingface.co/arun11karthik/cellsense-fim-0.5b}}
|
||||||
|
}
|
||||||
|
```
|
||||||
BIN
benchmarks.png
Normal file
BIN
benchmarks.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 84 KiB |
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- messages[0]['content'] }}
|
||||||
|
{%- else %}
|
||||||
|
{{- 'You are a helpful assistant.' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{{- '<|im_start|>' + message.role }}
|
||||||
|
{%- if message.content %}
|
||||||
|
{{- '\n' + message.content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if tool_call.function is defined %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- endif %}
|
||||||
57
config.json
Normal file
57
config.json
Normal file
@@ -0,0 +1,57 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"dtype": "bfloat16",
|
||||||
|
"eos_token_id": 151643,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 896,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 4864,
|
||||||
|
"layer_types": [
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 24,
|
||||||
|
"model_type": "qwen2",
|
||||||
|
"num_attention_heads": 14,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"num_key_value_heads": 2,
|
||||||
|
"pad_token_id": null,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"transformers_version": "5.5.0",
|
||||||
|
"use_cache": false,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"do_sample": false,
|
||||||
|
"eos_token_id": 151643,
|
||||||
|
"max_new_tokens": 2048,
|
||||||
|
"transformers_version": "5.5.0"
|
||||||
|
}
|
||||||
3
icon.png
Normal file
3
icon.png
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:d76b72e1f3a1c165377900dbd23fa3ff15f31797cfff0c827839b03f032f3ed4
|
||||||
|
size 116278
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:f9ff52f838035d9323fb1c0b0ceb3bc008bce5d9516e7cdf3b5059de23347e25
|
||||||
|
size 988097824
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
||||||
|
size 11421892
|
||||||
29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"is_local": false,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user