SmolLM2 1.7B Instruct DocstringGenerator

SmolLM2-1.7B-Instruct · DocstringGenerator

A fine-tuned 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 CLI — a fully local, LLM-powered tool that automatically writes and manages docstrings in your Python codebase.

GitHub License Python Base Model

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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

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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.

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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

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Training Pipeline

Stage 1 — Code Extraction

Raw Python source files were streamed from codeparrot/codeparrot-clean (~200 k samples). Each file passed a quality filter that rejected:

Filter	Threshold
Too few lines	< 3 non-empty lines
Minified code	avg line length > 150 chars
Low alphabetic ratio	< 15 % (binary / machine-generated)
Repetitive boilerplate	unique line ratio < 10 %
Oversized files	> 50 000 characters

Surviving files were parsed with LibCST 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. Describe semantic purpose and architectural role, not implementation details
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):

Hyperparameter	Value
Quantisation	4-bit NF4, double quant, fp16 compute
LoRA rank r	32
LoRA alpha α	64
LoRA dropout	0.2
LoRA bias	none
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer	AdamW 8-bit (bitsandbytes)
Learning rate	2e-4
LR schedule	Cosine with 5 % warmup
Weight decay	0.01
Batch size	8 per device
Gradient accumulation	8 steps → effective batch 64
Epochs	1
Max sequence length	1 024 tokens (95th-pct filter)
Validation split	1 % held-out, evaluated each epoch
Seed	1337

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

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Files

File	Description
smollm2_1_7b_instruct_merged-q8_0.gguf	Q8_0 GGUF for llama.cpp — recommended for local use
safetensors/model.safetensors	Merged fp16 weights
safetensors/config.json	HuggingFace model configuration
safetensors/tokenizer.json / safetensors/tokenizer_config.json	SmolLM2-1.7B-Instruct tokenizer

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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.

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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}}
}

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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)