Committed — Qwen3-0.6B (Q4_K_M GGUF) · default model
A small model fine-tuned to write Conventional Commits messages from a git diff. It runs locally on CPU via llama.cpp, so your code never leaves your machine.
This is the default, recommended model for Committed. It started as a controlled experiment — could a model a third the size of the original 1.7B do the same job? — and it essentially can: it matches the 1.7B on picking the right commit type and staying faithful to the diff, at ~⅓ the parameters, a ~397 MB download (vs ~1 GB), and faster local inference. The one honest trade is that it writes slightly vaguer messages (lower specificity), so the larger 1.7B GGUF remains available as the bigger sibling if you want maximum specificity.
This repo holds the merged, quantized GGUF used for serving. The training dataset, LoRA adapter, and source code are linked below.
The trained behavior depends on the exact prompt rendering used in training plus the GBNF grammar applied at decode time, so a bare llama-cpp-python prompt will not reproduce the evaluated output. Run it through the project's inference path instead.
Easiest — the CLI, which downloads this GGUF on first run (no token needed; it's public) and wires up the prompt + grammar for you:
git diff | committed uses this 0.6B model by default. To use the larger 1.7B instead:
git diff | committed --model 1.7b
Or call it through the repo's engine.py, the FastAPI endpoint, or the Gradio Space — all linked below. Full instructions and options are in the repo README.
Results
Evaluated against the un-tuned Qwen3-0.6B base on a 442-example test set, scored by an LLM judge (DeepSeek) on four axes; the judge was validated against 50 hand-rated examples. Headline numbers are reweighted to the true commit-type distribution.
The four-arm comparison — both base models, both fine-tunes, all judged by the same DeepSeek judge so the numbers are directly comparable:
Metric
0.6B base
0.6B fine-tune
1.7B base
1.7B fine-tune
Type accuracy
0.154
0.601
0.131
0.637
Type-correctness
0.296
0.726
0.296
0.778
Faithfulness
0.285
0.810
0.491
0.848
Completeness
0.353
0.729
0.543
0.776
Specificity
0.414
0.545
0.814
0.667
Conjunctive (all 4)
0.101
0.359
0.175
0.471
Graded mean (0–3)
0.777
2.094
1.447
2.139
feat-share of outputs
86.7%
9.7%
95.5%
8.4%
The finding: both base models "feat-collapse" — they label ~87–96% of all diffs feat regardless of content, scoring below a trivial always-guess-fix baseline (0.489) on type. Fine-tuning breaks the collapse on both. The 0.6B fine-tune lands within a few points of the 1.7B on type, faithfulness, completeness, and the graded mean (2.094 vs 2.139) — the gap is concentrated in specificity (0.545 vs 0.667): the 0.6B picks the right type and states accurate facts, but names the concrete mechanism less often.
Honest caveat on the judge: agreement with human raters is moderate-to-substantial on three axes (κ ≈ 0.56–0.61) but weakest on specificity (κ 0.34) — the very axis where 0.6B trails — so that gap carries the most judge uncertainty. These DeepSeek-judged numbers are not comparable to any earlier Gemini-judged figures; only the deltas within this all-DeepSeek table are valid.
Full before/after, the feat-collapse analysis, and the judge validation are in the eval writeup: FINDINGS_v1.md.
Trained with TRL. Dataset derived from CommitChronicle (Eliseeva et al., From Commit Message Generation to History-Aware Commit Message Generation, arXiv:2308.07655).
