---
license: apache-2.0
language:
- en
- ru
tags:
- code
- documentation
- summarization
- distillation
- qwen3
- gguf
- llama.cpp
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
library_name: gguf
---

# 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. Available quants: `Q5_K_M` (~2.8 GB, highest fidelity),
`Q4_K_M` (~2.4 GB, recommended), `Q3_K_M` (~2.0 GB, for 6 GB-VRAM GPUs). 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|>`.

```bash
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).

## Speed

Measured on a laptop RTX 5060 (8 GB), llama.cpp CUDA, Q4_K_M, `n_ctx=8192`, `n_batch=2048`,
single stream — the way this model is actually used, since a hierarchical summary is one prompt
rolling up its children and has nothing to batch against.

| Workload | Input → output | Rate |
|---|---|---|
| Hierarchical summary | 250 → 250 tok | **103 tok/s** |
| Long-form entity doc | 900 → 1500 tok | **95 tok/s** |
| Prefill (both) | — | **~3 300 tok/s** |

**Memory:** ~4.7 GB resident at `n_ctx=8192` — 2.4 GB weights plus KV and compute buffers. On an
8 GB card that leaves room for one small co-resident model, not two; decode collapses to roughly a
third of these rates if the card is oversubscribed, so give it the headroom rather than the extra
worker.

## How it was made

- **Base:** [`Qwen/Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B) (Apache-2.0), 36 layers,
  ChatML, 151 936-token vocab.
- **Teacher:** [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/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.