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Model: arun11karthik/cellsense-fim-0.5b Source: Original Platform
2026-09-08 05:18:17 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-Coder-0.5B
+language:
+- code
+tags:
+- cellsense
+- fim
+- fill-in-the-middle
+- code-completion
+- jupyter
+- notebook
+- code
+- qwen2
+model-index:
+- name: cellsense-fim-0.5b
+  results:
+  - task:
+      type: text-generation
+      name: Fill-in-the-Middle Code Completion
+    dataset:
+      name: CellSense Jupyter FIM (private)
+      type: cellsense-fim-large
+      split: test
+    metrics:
+    - type: edit_similarity
+      value: 0.641
+      name: Edit Similarity
+    - type: bleu
+      value: 43.09
+      name: BLEU
+    - type: codebleu
+      value: 0.346
+      name: CodeBLEU
+    - type: token_accuracy
+      value: 0.868
+      name: Token Accuracy
+    - type: bits_per_byte
+      value: 0.254
+      name: Bits per Byte
+---
+
+<p align="center">
+  <img src="icon.png" alt="CellSense" width="180" height="180" />
+</p>
+
+# CellSense-FIM 0.5B
+
+<!-- TODO: replace OWNER/REPO with your GitHub repo and PACKAGE with your PyPI package name -->
+[![Hugging Face Space](https://img.shields.io/badge/Hugging%20Face-CellSense--FIM-FFD21E?logo=huggingface&logoColor=black)](https://huggingface.co/spaces/arun11karthik/cellsense-fim)
+[![GitHub](https://img.shields.io/badge/GitHub-CellSense-181717?logo=github)](https://github.com/arunkarthik11/cellsense)
+[![PyPI](https://img.shields.io/pypi/v/PACKAGE?logo=pypi&label=pip%20install)](https://pypi.org/project/jupyterlab-cellsense/)
+
+**CellSense-FIM 0.5B** is the smallest member of the CellSense-FIM model family — a set of
+**long-context, fill-in-the-middle (FIM) code-completion models built specifically for Jupyter
+notebooks.** It is fine-tuned from [`Qwen/Qwen2.5-Coder-0.5B`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B)
+on the (private) **CellSense FIM** dataset and supports a **32K-token context window**.
+
+Unlike general code models that treat a notebook as a flat file, CellSense-FIM models are trained 
+on a more detailed context, that actually matters when you complete a cell:
+
+- **🗂️ Repository-aware** — the model is trained with surrounding files from the same repository in
+  context, so completions respect helpers, constants, and conventions defined elsewhere in the project.
+- **🔗 Local-import-aware** — when your notebook imports from a sibling module, the relevant source and signatures are
+  pulled into context, so the model completes calls to *your* code with the right signatures, not a
+  plausible guess.
+- **🎯 Task-aware** — the files you have been reading and editing are almost always the most relevant to
+  what you are working on right now. The context conditions on this context, so completions reflect where 
+  your attention has actually been — not just what happens to be open in the active tab.
+
+The models are best paired with the **CellSense Jupyter Lab Plugin**, which assembles repository, local-import,
+and task context into the exact format the model was trained on — so the model consumes it natively with
+no prompt engineering on your part.
+
+## Model family
+
+| Model | Base | Params | Context |
+|-------|------|:------:|:-------:|
+| **CellSense-FIM 0.5B** (this model) | Qwen2.5-Coder-0.5B | 0.5B | 32K |
+| CellSense-FIM 1.5B | Qwen2.5-Coder-1.5B | 1.5B | 32K |
+| CellSense-FIM 3B   | Qwen2.5-Coder-3B   | 3B   | 32K |
+| CellSense-FIM 7B   | Qwen2.5-Coder-7B   | 7B   | 32K |
+
+## Evaluation
+
+Evaluated on the held-out `test` split of the CellSense FIM dataset. CellSense-FIM is compared against its base model
+(`Qwen2.5-Coder-0.5B`) and a same-size general model (`Qwen3-0.6B`).
+
+![FIM completion-quality benchmarks](./benchmarks.png)
+
+Fine-tuning on notebook-native FIM context yields large gains on the metrics that track *real completion
+quality* — **edit similarity (0.11 → 0.64)** and **BLEU (4.7 → 43.1)** — while keeping token accuracy
+strong. At 0.5B, CodeBLEU stays close to the base model; the larger family members improve it outright.
+
+### Likelihood metric — Bits per Byte (lower is better)
+
+| Model | Bits per Byte ↓ |
+|-------|:---------------:|
+| Qwen2.5-Coder-0.5B (base) | 0.305 |
+| Qwen3-0.6B (base) | 1.082 |
+| **CellSense-FIM 0.5B (ours)** | **0.254** |
+
+## Usage
+
+### Prompt format (FIM)
+
+The model uses the Qwen2.5-Coder FIM sentinels. For a single-file completion:
+
+```
+<|fim_prefix|>{code before the cursor}<|fim_suffix|>{code after the cursor}<|fim_middle|>
+```
+
+For repository / local-import-aware completion, prepend the relevant files before the FIM block:
+
+```
+<|repo_name|>{repo}<|file_sep|>{path/to/helper.py}
+{contents of helper.py}
+<|file_sep|>{path/to/notebook_cell}
+<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>
+```
+
+> 💡 In practice you don't assemble this by hand — the **CellSense JupyterLab plugin** builds the
+> repository-, import-, and task-aware context and emits exactly this format (see
+> [Serving with vLLM + CellSense](#serving-with-vllm--cellsense) below).
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "arun11karthik/cellsense-fim-0.5b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+prefix = "import pandas as pd\ndf = pd.read_csv('data.csv')\n"
+suffix = "\ndf.head()\n"
+prompt = f"<|fim_prefix|>{prefix}<|fim_suffix|>{suffix}<|fim_middle|>"
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+## Serving with vLLM + CellSense
+
+The intended way to use this model is to serve it with vLLM and point the
+[**CellSense JupyterLab plugin**](https://github.com/arunkarthik11/CellSense) at the server over its
+OpenAI-compatible API. CellSense handles all of the repository-, import-, and task-aware context
+assembly and the FIM prompt formatting for you.
+
+### 1. Serve the model with vLLM
+
+vLLM exposes an OpenAI-compatible endpoint, which is exactly what CellSense's `openai_compatible`
+provider expects:
+
+```bash
+pip install vllm
+
+vllm serve arun11karthik/cellsense-fim-0.5b \
+  --served-model-name cellsense-fim-0.5b \
+  --max-model-len 32768 \
+  --port 8000
+```
+
+This serves the API at `http://localhost:8000/v1`.
+
+### 2. Point CellSense at the vLLM server
+
+In the CellSense settings panel (left sidebar in JupyterLab), open **Basic Settings** and configure the
+**OpenAI Compatible** provider:
+
+| Setting | Value |
+|---------|-------|
+| **Provider** | `OpenAI Compatible` |
+| **Base URL** | `http://localhost:8000/v1` |
+| **API Key** | any non-empty string (vLLM ignores it, e.g. `EMPTY`) |
+| **Model** | `cellsense-fim-0.5b` (must match `--served-model-name`) |
+| **Model Family** | `qwen2.5-coder` |
+
+Click **Save & Apply**, then start typing in a notebook cell — ghost-text completions from your
+local model appear inline. Press **Tab** to accept.
+
+> 🌐 The same setup works for a **remote** vLLM server: serve the model on your GPU box, expose port
+> `8000`, and set CellSense's **Base URL** to `http://<host>:8000/v1`.
+
+### Raw API check (optional)
+
+To confirm the endpoint works before wiring up CellSense, query it directly with the FIM prompt:
+
+```bash
+curl http://localhost:8000/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "cellsense-fim-0.5b",
+    "prompt": "<|fim_prefix|>import pandas as pd\ndf = pd.read_csv(\"data.csv\")\n<|fim_suffix|>\ndf.head()\n<|fim_middle|>",
+    "max_tokens": 128,
+    "temperature": 0.0
+  }'
+```
+
+## Ollama
+
+For fully local, no-GPU-required inference, GGUF builds are published at
+[**`arun11karthik/cellsense-fim-0.5b-GGUF`**](https://huggingface.co/arun11karthik/cellsense-fim-0.5b-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` | ~420 MB | Good quality / size trade-off | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q5_K_M` |
+| `Q8_0`   | ~531 MB | Near-lossless 8-bit quantization | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-GGUF:Q8_0` |
+| `BF16`   | ~994 MB | Full bfloat16 precision — highest quality (recommended) | `ollama run hf.co/arun11karthik/cellsense-fim-0.5b-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-0.5b-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-0.5b-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-0.5b-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-0.5b-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 }
+}'
+```
+
+## Training
+
+- **Base model:** `Qwen/Qwen2.5-Coder-0.5B`
+- **Dataset:** the private **CellSense FIM** dataset — repository-, local-import-, and task-aware FIM examples mined from Jupyter notebooks
+- **Objective:** fill-in-the-middle (FIM) code completion
+- **Context length:** 32,768 tokens
+- **Checkpoint:** best-validation checkpoint
+
+## Training data & privacy
+
+CellSense-FIM was fine-tuned on a private corpus of fill-in-the-middle examples mined from Jupyter
+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}}
+}
+```
--- a/benchmarks.png
+++ b/benchmarks.png
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -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 %}
--- a/config.json
+++ b/config.json
@@ -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
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "5.5.0"
+}
--- a/icon.png
+++ b/icon.png
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+size 116278
--- a/model.safetensors
+++ b/model.safetensors
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+oid sha256:f9ff52f838035d9323fb1c0b0ceb3bc008bce5d9516e7cdf3b5059de23347e25
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--- a/tokenizer.json
+++ b/tokenizer.json
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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -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
+}