code-daemon-summary-v1

Compact bilingual (English / Russian) code-documentation generator — a 4B GGUF model that writes:

• one-sentence entity descriptions for a source file (functions, classes, fields) as a clean markdown bullet list;
• module overviews — short prose plus ASCII architecture / data-flow diagrams;
• hierarchical codebase summaries — subsystem, product-level and whole-project digests built from smaller summaries.

Runs anywhere llama.cpp runs; Q4_K_M fits in ~2.5 GB. The output language follows the request — both languages were distilled first-class, not translated.

Task	Output shape
Entity documentation	- **Name**: one-sentence description. per entity
Module overview	## Overview prose + ## Architecture / ## Flow ASCII diagrams
Hierarchical summaries	paragraph-length subsystem/product/strategy digests

Quick start (llama.cpp)

The bundled chat template already pins the model's non-thinking mode — use it as a normal ChatML model. If you build raw prompts yourself, end them with the assistant tag followed by an empty think block (<|im_start|>assistant\n<think>\n\n</think>\n\n) — that is the format the model was trained with. Greedy decoding (temperature 0) recommended; stop on <|im_end|>.

llama-cli -m code-daemon-summary-v1-Q4_K_M.gguf -c 8192 --temp 0 \
  -p '<|im_start|>system
You write one-sentence descriptions for code entities of a single file. Output ONLY a markdown bullet list, ONE bullet per entity: - **<EntityName>**: <one-sentence description>.<|im_end|>
<|im_start|>user
Entities: parseArray, encodeValue. File excerpt: <...><|im_end|>
<|im_start|>assistant
<think>

</think>

'

Evaluation

Held-out prompts via a deterministic content-hash split (provably never trained on), sampled across the full bilingual corpus; reference = the 7B teacher's output on the same prompts.

Slice	n	ROUGE-L	sem-cos*	empty outputs
Entity docs, mixed EN+RU	300	0.618	0.904	0
Entity docs, EN-only sample	300	0.636	0.897	0
Hierarchical summaries	7	0.331	0.893	0

* all-MiniLM-L6-v2 cosine between student and teacher outputs — the paraphrase-aware signal; token metrics understate free-form prose.

Language fidelity: 56/57 Russian-reference examples answered in Russian, 242/243 English in English. Residual gap to the teacher: slightly more verbose (len-ratio ~1.3–1.6).

How it was made

• Base: Qwen/Qwen3-4B (Apache-2.0), 36 layers, ChatML, 151 936-token vocab.
• Teacher: Qwen/Qwen2.5-7B-Instruct.
• Method: sequence-level knowledge distillation (SeqKD) — LoRA SFT on ~11K teacher (prompt → response) traces, 3 epochs, prompt tokens masked, then merged into the base. An on-policy DistiLLM-2 refinement pass was also trained and rejected on evaluation (it degraded long-prompt behaviour); v1 is the SeqKD checkpoint.
• The model powers the long-output documentation stages of a code-intelligence daemon; it is a purpose-built component, not a general assistant — outside this task distribution its behaviour is undefined.

Training data

Teacher traces generated over a mixed corpus: ~90 open-source repositories (Zig, C/C++, C#, TypeScript/JS, Python, Go, Rust, Kotlin, Swift, Java) for English and a commercial C#/TS/Python codebase for Russian. ~11K kept traces after dedup and corruption filtering. No third-party labeled dataset is used.

License & attribution

Apache-2.0 — matches the Qwen3-4B base and the Qwen2.5-7B-Instruct teacher (both Alibaba / Qwen team, Apache-2.0). Not legal advice. Base and teacher © the Qwen team; please also honour their model cards.