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Model: yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- code
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- python
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- docstring
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- documentation
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- code-generation
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- lora
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- qlora
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- smollm2
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- instruct
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- causal-lm
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base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
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pipeline_tag: text-generation
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model-index:
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- name: SmolLM2-1.7B-Instruct-DocstringGenerator
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results: []
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datasets:
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- codeparrot/codeparrot-clean
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---
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# SmolLM2-1.7B-Instruct · DocstringGenerator
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> A fine-tuned **[SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)** specialised in writing **concise, high-level Python docstrings** for functions, methods and classes.
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> This model is the backbone of the **[PyDoctor](https://github.com/yezdata/pydoctor)** CLI — a fully local, LLM-powered tool that automatically writes and manages docstrings in your Python codebase.
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[](https://github.com/yezdata/pydoctor)
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[](LICENSE)
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[](https://www.python.org/)
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[](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
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---
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## Intended Use
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The model generates **summary-style docstrings** — single-paragraph, plain-English descriptions of a Python code block's purpose and architectural role. It does **not** produce `Args:`, `Returns:`, or `Raises:` sections by design.
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**Suitable for:**
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- Automated docstring generation in CI/CD pipelines
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- Interactive IDE plugins
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- Local, privacy-preserving documentation workflows via llama.cpp / GGUF
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**Not suitable for:**
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- General-purpose code generation
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- Generating full NumPy/Google-style docstrings with parameter tables (explicitly omitted)
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- Non-Python languages
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---
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## Quick Start
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### With llama.cpp (GGUF · recommended for local use)
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```bash
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# Download the Q8_0 GGUF
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huggingface-cli download \
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yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator \
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smollm2_1_7b_instruct_merged-q8_0.gguf \
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--local-dir ./models
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# Run inference
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llama-cli \
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-m ./models/smollm2_1_7b_instruct_merged-q8_0.gguf \
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--chat-template chatml \
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-p "..."
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```
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> **Tip:** The [PyDoctor CLI](https://github.com/yezdata/pydoctor) handles prompt construction, parsing, and atomic file rewrites out of the box.
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---
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## Prompt Format (ChatML)
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The model uses the **ChatML** template native to SmolLM2-Instruct:
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```
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<|im_start|>system
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{SYSTEM_PROMPT}<|im_end|>
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<|im_start|>user
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CONTEXT
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{context_code}
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TARGET CODE
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{target_code}<|im_end|>
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<|im_start|>assistant
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```
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The model then generates only the raw docstring text, terminated by `<|im_end|>`.
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**Context definition:**
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- **function** target -> context = "Independent code block"
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- **method** target → context = `__init__` signature of its enclosing class
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- **class** target → context = signatures of its methods
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---
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## Training Pipeline
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### Stage 1 — Code Extraction
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Raw Python source files were streamed from **[codeparrot/codeparrot-clean](https://huggingface.co/datasets/codeparrot/codeparrot-clean)** (~200 k samples). Each file passed a quality filter that rejected:
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| Filter | Threshold |
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|---|---|
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| Too few lines | < 3 non-empty lines |
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| Minified code | avg line length > 150 chars |
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| Low alphabetic ratio | < 15 % (binary / machine-generated) |
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| Repetitive boilerplate | unique line ratio < 10 % |
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| Oversized files | > 50 000 characters |
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Surviving files were parsed with **[LibCST](https://libcst.readthedocs.io/)** producing `(target, context)` pairs.
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### Stage 2 — Synthetic Docstring Generation
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`(target, context)` pairs were labelled in parallel using **DeepSeek V4 Flash** (via OpenRouter):
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The teacher-model system prompt enforced:
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1. Describe semantic purpose and architectural role, not implementation details
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2. Use context to disambiguate class membership
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### Stage 3 — Instruct Data Preparation & Tokenisation
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Synthetic batches were assembled into ChatML prompt/completion pairs:
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```python
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prompt = (
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f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
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f"<|im_start|>user\nCONTEXT\n{context}\n\nTARGET CODE\n{target}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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completion = f"{docstring}<|im_end|>"
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```
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Labels were constructed so that **only completion tokens** are trained on — prompt tokens are masked from cross-entropy loss.
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### Stage 4 — QLoRA Fine-tuning
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Fine-tuning was performed on Kaggle kernels (`instruct_finetune.py`):
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| Hyperparameter | Value |
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|---|---|
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| Quantisation | 4-bit NF4, double quant, fp16 compute |
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| LoRA rank `r` | 32 |
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| LoRA alpha `α` | 64 |
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| LoRA dropout | 0.2 |
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| LoRA bias | none |
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| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
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| Optimizer | AdamW 8-bit (bitsandbytes) |
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| Learning rate | 2e-4 |
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| LR schedule | Cosine with 5 % warmup |
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| Weight decay | 0.01 |
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| Batch size | 8 per device |
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| Gradient accumulation | 8 steps → effective batch 64 |
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| Epochs | 1 |
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| Max sequence length | 1 024 tokens (95th-pct filter) |
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| Validation split | 1 % held-out, evaluated each epoch |
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| Seed | 1337 |
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Loss = next-token cross-entropy, **prompt tokens ignored** via label mask.
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### Stage 5 — LoRA Merge & GGUF Export
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After training, LoRA adapters were merged back into the base model weights and converted to **Q8_0 GGUF** using `llama.cpp`:
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```
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LoRA adapter (epoch 1, safetensors)
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│
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▼ merge_and_unload()
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│
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merged fp16 safetensors
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│
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▼ llama.cpp convert_hf_to_gguf.py --outtype q8_0
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▼
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smollm2_1_7b_instruct_merged-q8_0.gguf
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```
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---
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## Files
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| File | Description |
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| `smollm2_1_7b_instruct_merged-q8_0.gguf` | Q8_0 GGUF for llama.cpp — recommended for local use |
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| `safetensors/model.safetensors` | Merged fp16 weights |
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| `safetensors/config.json` | HuggingFace model configuration |
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| `safetensors/tokenizer.json` / `safetensors/tokenizer_config.json` | SmolLM2-1.7B-Instruct tokenizer |
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---
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## Limitations & Bias
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- **Summary-only style:** the model is trained to output a single-paragraph summary. It will not produce `Args:` / `Returns:` sections.
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- **Python only:** trained exclusively on Python source code from codeparrot-clean.
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- **Context dependency:** quality improves when the correct context string is provided. Passing an empty context for class methods may reduce coherence.
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- **Teacher model bias:** docstring style reflects DeepSeek V4 Flash's preferences filtered through the strict prompt rules. Unusual code idioms may yield generic descriptions.
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- **Not a general assistant:** the model is heavily specialised and will likely perform poorly on tasks other than docstring generation.
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---
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## Citation
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```bibtex
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@misc{pydoctor2026,
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author = {yezdata},
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title = {PyDoctor: Local LLM-powered Python Docstring Generator},
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year = {2026},
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howpublished = {\url{https://github.com/yezdata/pydoctor}},
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note = {Fine-tuned SmolLM2-1.7B-Instruct model available at
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\url{https://huggingface.co/yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator}}
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}
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```
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---
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## License
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This model is released under the **Apache 2.0** license, matching the base `SmolLM2-1.7B-Instruct` model.
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Training data originates from `codeparrot/codeparrot-clean` (MIT)
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