Conventional Commits message generator in Brazilian Portuguese (PT-BR), fine-tuned from Qwen 2.5 Coder 7B. Receive a "git diff", return a single-line commit message in Conventional Commits format.

Details

Updated 1 month ago

1 month ago

f6291a76c627 · 4.7GB ·

model

archqwen2

parameters7.62B

quantizationQ4_K_M

4.7GB

system

Você é um gerador de mensagens de commit no padrão Conventional Commits. Receba um git diff e res

280B

params

{ "num_ctx": 8192, "repeat_penalty": 1.05, "stop": [ "<|im_end|>", "<|en

127B

template

{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|> {{- else if .M

1.6kB

seshat-commit

Conventional Commits message generator in Brazilian Portuguese (PT-BR), fine-tuned from Qwen 2.5 Coder 7B.

Receive a git diff, return a single-line commit message in Conventional Commits format. Trained on 4869 real commits from the author’s repositories with strict quality filters. Designed to be used inside Seshat, an automated commit CLI.

Model page: https://ollama.com/juniormartinxo/seshat-commit

Quick start

ollama pull juniormartinxo/seshat-commit

# basic usage:
git diff --cached | ollama run juniormartinxo/seshat-commit

# with Seshat (recommended):
seshat config --provider ollama --model juniormartinxo/seshat-commit
seshat commit --yes

Example:

input  → diff --git a/src/foo.rs b/src/foo.rs
         +log::info!("starting bar");

output → feat(foo): adicionar log de início na função bar

Specs


Base model	Qwen 2.5 Coder 7B Instruct
Method	QLoRA (4-bit base + LoRA r=16, α=16)
Trainable params	40M of 7.6B (0.53%)
Quantization	Q4_K_M (4.4 GB)
Context	8192 tokens
Languages	PT-BR (primary), EN/ES (limited)
Final loss	0.2768
Training time	~30 min on RTX 5070 Ti (16 GB)
Dataset	4869 train + 256 eval, filtered from 13525 raw commits

What it does well

Generates Conventional Commits in PT-BR (feat, fix, chore, docs, refactor, perf, test, build, ci, style, revert)
Infers correct scope from file paths (feat(api), fix(rtk), chore(release))
Mixed PT-BR/EN naturally for technical terms (bump version, endpoint)
Handles Rust, Python, TypeScript, JavaScript, Go, Shell, Markdown
Validates against Conventional Commits spec on ~95–98% of typical diffs

What it doesn’t do

Long-form text (release notes, PR descriptions, code generation)
Conversational chat — it’s specialized for diff → commit
English-only output — output is PT-BR by default

Recommended parameters

The included Modelfile sets:

PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER num_ctx 8192
PARAMETER repeat_penalty 1.05

SYSTEM "Você é um gerador de mensagens de commit no padrão Conventional Commits.
Receba um git diff e responda apenas com a mensagem de commit, sem explicação.
Use PT-BR no corpo quando aplicável.
Tipo válido: feat, fix, chore, docs, refactor, perf, test, build, ci, style, revert."

Lower temperature (0.0–0.3) for production, higher (0.5–0.8) if you want more variation.

Training data

Extracted from public + private repositories of the author (~14k commits → 5k after filtering). Filters applied:

Only Conventional Commits format (regex ^(feat|fix|chore|...)(scope)?(!)?: .+)
Removed: WIP, Revert, fixup!, squash!, [skip ci], bot authors, whitespace-only diffs, generic subjects (update, fix, tweak…), placeholders (<TODO>, XXX), URL-only subjects, filename-only subjects
Deduplicated by subject + diff hash
Capped per type to balance distribution
Languages observed: Rust (40%), TypeScript (25%), Python (15%), JS/Go/Shell (20%)

Hardware requirements

Use case	Min	Recommended
Inference (Q4_K_M)	6 GB VRAM or 8 GB RAM (CPU)	8 GB VRAM
Fine-tune from this base	RTX 3090 / 4070 Ti / 5070 Ti (16 GB)	24 GB

Runs comfortably on a single consumer GPU. CPU inference works but slow (~10–20 tokens/s on modern x86).

License

The base model (Qwen 2.5 Coder) is released under the Qwen Research License. This fine-tune inherits the same license.

The fine-tuned LoRA adapter and dataset extraction pipeline are MIT.

Source / pipeline

Full reproducible training pipeline at: https://github.com/juniormartinxo/seshat (see scripts/dataset/ and scripts/training/).

Re-train on your own commit history in ~1 hour:

make junior              # extract + normalize your commits
python train.py          # LoRA fine-tune
ollama create my-model -f Modelfile

Limitations & known issues

Subjects > 100 chars sometimes get cut mid-sentence
Rare diffs (heavy renames, binary deletions) may produce English fallback
Bias towards author’s style: terse, lowercase-after-colon, occasional EN/PT mix
No multi-paragraph commit bodies (always single-line subject)

Use with Seshat

Seshat is a CLI that automates the commit workflow:

seshat init                                              # configure project
seshat config --provider ollama --model juniormartinxo/seshat-commit
git add .
seshat commit --yes                                      # generate + commit
seshat flow 5 --yes                                      # batch-commit modified files
seshat bench agents --agents ollama --show-samples 3     # benchmark vs Codex/Claude