yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator

SmolLM2-1.7B-Instruct · DocstringGenerator

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. 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.

   

Intended Use

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.

Suitable for:

• —Automated docstring generation in CI/CD pipelines
• —Interactive IDE plugins
• —Local, privacy-preserving documentation workflows via llama.cpp / GGUF

Not suitable for:

• —General-purpose code generation
• —Generating full NumPy/Google-style docstrings with parameter tables (explicitly omitted)
• —Non-Python languages

Quick Start

With llama.cpp (GGUF · recommended for local use)

# Download the Q8_0 GGUF
huggingface-cli download \
  yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator \
  smollm2_1_7b_instruct_merged-q8_0.gguf \
  --local-dir ./models

# Run inference
llama-cli \
  -m ./models/smollm2_1_7b_instruct_merged-q8_0.gguf \
  --chat-template chatml \
  -p "..."

Tip: The PyDoctor CLI handles prompt construction, parsing, and atomic file rewrites out of the box.

Prompt Format (ChatML)

The model uses the ChatML template native to SmolLM2-Instruct:

<|im_start|>system
{SYSTEM_PROMPT}<|im_end|>
<|im_start|>user
CONTEXT
{context_code}

TARGET CODE
{target_code}<|im_end|>
<|im_start|>assistant

The model then generates only the raw docstring text, terminated by <|im_end|>.

Context definition:

• —function target -> context = "Independent code block"
• —method target → context = __init__ signature of its enclosing class
• —class target → context = signatures of its methods

Training Pipeline

Stage 1 — Code Extraction

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:

Surviving files were parsed with [LibCST](https://libcst.readthedocs.io/) producing (target, context) pairs.

Stage 2 — Synthetic Docstring Generation

(target, context) pairs were labelled in parallel using DeepSeek V4 Flash (via OpenRouter):

The teacher-model system prompt enforced:

1. 1.Describe semantic purpose and architectural role, not implementation details
2. 2.Use context to disambiguate class membership

Stage 3 — Instruct Data Preparation & Tokenisation

Synthetic batches were assembled into ChatML prompt/completion pairs:

prompt = (
    f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
    f"<|im_start|>user\nCONTEXT\n{context}\n\nTARGET CODE\n{target}<|im_end|>\n"
    f"<|im_start|>assistant\n"
)
completion = f"{docstring}<|im_end|>"

Labels were constructed so that only completion tokens are trained on — prompt tokens are masked from cross-entropy loss.

Stage 4 — QLoRA Fine-tuning

Fine-tuning was performed on Kaggle kernels (instruct_finetune.py):

Loss = next-token cross-entropy, prompt tokens ignored via label mask.

Stage 5 — LoRA Merge & GGUF Export

After training, LoRA adapters were merged back into the base model weights and converted to Q8_0 GGUF using llama.cpp:

LoRA adapter (epoch 1, safetensors)
        │
        ▼  merge_and_unload()
        │
merged fp16 safetensors
        │
        ▼  llama.cpp convert_hf_to_gguf.py --outtype q8_0
        ▼
smollm2_1_7b_instruct_merged-q8_0.gguf

Files

Limitations & Bias

• —Summary-only style: the model is trained to output a single-paragraph summary. It will not produce Args: / Returns: sections.
• —Python only: trained exclusively on Python source code from codeparrot-clean.
• —Context dependency: quality improves when the correct context string is provided. Passing an empty context for class methods may reduce coherence.
• —Teacher model bias: docstring style reflects DeepSeek V4 Flash's preferences filtered through the strict prompt rules. Unusual code idioms may yield generic descriptions.
• —Not a general assistant: the model is heavily specialised and will likely perform poorly on tasks other than docstring generation.

Citation

@misc{pydoctor2026,
  author       = {yezdata},
  title        = {PyDoctor: Local LLM-powered Python Docstring Generator},
  year         = {2026},
  howpublished = {\url{https://github.com/yezdata/pydoctor}},
  note         = {Fine-tuned SmolLM2-1.7B-Instruct model available at
                  \url{https://huggingface.co/yezdata/SmolLM2-1.7B-Instruct-DocstringGenerator}}
}

License

This model is released under the Apache 2.0 license, matching the base SmolLM2-1.7B-Instruct model. Training data originates from codeparrot/codeparrot-clean (MIT)