Update README.md
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README.md
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A small, fast model specialized to turn a git diff into a concise, English commit message. Built on top of `google/gemma-3-270m-it` and fine-tuned with LoRA using MLX on macOS.
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## What this model expects (most important)
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- Input type: a unified git diff as plain text.
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- Wrap the diff in a Markdown code fence labeled `diff` for best results
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```
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```diff
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<your unified git diff here>
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```
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```
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- The diff should look like the output of `git diff --no-color` (hunk headers like `@@`, `+`/`-` line prefixes, file headers, etc.).
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- Keep diffs reasonably sized. The training/CLI path truncates diffs to ~3,000 characters and trains/infers with a context window of ~2,048 tokens. Extremely large diffs should be summarized or sampled.
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- Language of response: English only. The system prompt enforces English output.
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### Chat template (Gemma 3)
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The model was trained and inferred using Gemma’s chat template. Conceptually:
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@@ -52,83 +98,185 @@ Training data (chat format) examples were stored like:
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## Quick usage
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###
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- From a staged diff in your current repo:
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```bash
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python commit_msg_cli.py run --from-git --staged --adapter \
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--model google/gemma-3-270m-it \
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--adapter-path ./adapters
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```
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```bash
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python commit_msg_cli.py run --diff path/to/diff.txt --adapter \
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--model google/gemma-3-270m-it \
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--adapter-path ./adapters
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```
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The CLI will wrap your diff with the expected prompt/template and return a single-line message.
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### Programmatic (MLX)
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```python
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```
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## Examples
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Input (user message content):
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```diff
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diff --git a/
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index
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--- a/
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+++ b/
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@@ -
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```
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Possible outputs:
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-
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## Training summary
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- Base model: `google/gemma-3-270m-it` (Gemma 3, 270M, instruction-tuned).
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- Method: LoRA fine-tuning with MLX (`mlx_lm lora`). Prompt masking was enabled so the model learns from the assistant response.
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- Context/config highlights: max sequence length ~2048 tokens; diffs truncated to ~3,000 characters during preprocessing/inference to be model-friendly.
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## Evaluation
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@@ -160,4 +308,4 @@ The repository’s `format_commit_message_prompt` builds the correct prompt for
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## License and credits
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- Base model: Google Gemma 3 (`google/gemma-3-270m-it`). Use subject to the Gemma license terms.
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- Fine-tuning code: MLX and utilities in this repository. See repository license for details.
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A small, fast model specialized to turn a git diff into a concise, English commit message. Built on top of `google/gemma-3-270m-it` and fine-tuned with LoRA using MLX on macOS.
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## Requirements
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- macOS with Apple Silicon (for MLX)
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- Python 3.8+
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- Required packages:
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```bash
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pip install mlx-lm transformers
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```
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## What this model expects (most important)
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- Input type: a unified git diff as plain text.
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- Wrap the diff in a Markdown code fence labeled `diff` for best results.
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- The diff should look like the output of `git diff --no-color` (hunk headers like `@@`, `+`/`-` line prefixes, file headers, etc.).
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- Keep diffs reasonably sized. The training/CLI path truncates diffs to ~3,000 characters and trains/infers with a context window of ~2,048 tokens. Extremely large diffs should be summarized or sampled.
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- Language of response: English only. The system prompt enforces English output.
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### Training Data Format
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This model was trained on the `data/train_gpt-oss-20b.jsonl` dataset in this repository. The training data uses Gemma's chat template format with the following exact structure:
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**User prompt format (as seen in training data):**
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```
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Generate a concise and descriptive commit message for this git diff:
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```diff
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diff --git a/src/ossos-pipeline/scripts/update_astrometry.py b/src/ossos-pipeline/scripts/update_astrometry.py
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index <HASH>..<HASH> 100644
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--- a/src/ossos-pipeline/scripts/update_astrometry.py
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+++ b/src/ossos-pipeline/scripts/update_astrometry.py
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@@ -159,8 +159,11 @@ def recompute_mag(mpc_in):
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cutout = image_slice_downloader.download_cutout(reading, needs_apcor=True)
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cutout.zmag = new_zp
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+ if math.fabs(new_zp - old_zp) > 0.3:
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+ logging.warning("Large change in zeropoint detected: {} -> {}".format(old_zp, new_zp))
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try:
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- (x, y, mag, merr) = cutout.get_observed_magnitude(zmag=old_zp)
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+ (x, y, mag, merr) = cutout.get_observed_magnitude(zmag=new_zp)
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(x, y) = cutout.get_observed_coordinates((x, y))
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except:
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logging.warn("Failed to do photometry.")
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```
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```
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**Important:** To get the best results, match this exact format including:
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- The instruction text: "Generate a concise and descriptive commit message for this git diff:"
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- The double newline after the instruction
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- The diff wrapped in triple backticks with `diff` language tag
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- Hash placeholders shown as `<HASH>..<HASH>` in the diff headers
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### Chat template (Gemma 3)
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The model was trained using Gemma's chat template with the system prompt enforcing English-only responses. The conceptual structure is:
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- system: "You are a helpful assistant that generates git commit messages. Always respond in English only. Do not use any other language."
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- user: The exact format shown above
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- assistant: single-line commit message (target)
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### Chat template (Gemma 3)
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The model was trained and inferred using Gemma’s chat template. Conceptually:
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## Quick usage
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### Python Script (MLX)
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Here's a complete standalone script to generate commit messages using this model:
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```python
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#!/usr/bin/env python3
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"""
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Standalone script to generate git commit messages using the fine-tuned Gemma model.
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Requires: mlx-lm, transformers
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Install with: pip install mlx-lm transformers
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"""
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import subprocess
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import sys
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from mlx_lm import load, generate
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from transformers import AutoTokenizer
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def get_staged_diff():
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"""Get the staged git diff from the current repository."""
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try:
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result = subprocess.run(
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['git', 'diff', '--staged', '--no-color'],
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capture_output=True, text=True, check=True
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)
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return result.stdout.strip()
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except subprocess.CalledProcessError:
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print("Error: Could not get git diff. Make sure you're in a git repository with staged changes.")
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return None
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def format_prompt(diff_text, tokenizer):
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"""Format the diff into the exact training data format."""
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system_prompt = "You are a helpful assistant that generates git commit messages. Always respond in English only. Do not use any other language."
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user_message = f"Generate a concise and descriptive commit message for this git diff:\n\n```diff\n{diff_text}\n```"
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# Format using Gemma chat template
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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return prompt
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def generate_commit_message(diff_text, model_path="your-username/git-diff-to-commit-gemma-3-270m"):
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"""Generate a commit message from a git diff."""
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# Load model and tokenizer
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print("Loading model...")
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model, mlx_tokenizer = load(model_path)
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hf_tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-270m-it")
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# Format the prompt
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prompt = format_prompt(diff_text, hf_tokenizer)
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# Generate response
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print("Generating commit message...")
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response = generate(
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model,
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mlx_tokenizer,
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prompt=prompt,
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max_tokens=100,
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temp=0.7,
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top_p=0.9,
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verbose=False
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)
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# Extract just the generated part (after the prompt)
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generated_text = response[len(prompt):].strip()
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# Return the first non-empty line
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lines = [line.strip() for line in generated_text.split('\n') if line.strip()]
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return lines[0] if lines else "Unable to generate commit message"
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def main():
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"""Main function - can be used with staged diff or provided diff text."""
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if len(sys.argv) > 1:
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# Use provided diff file
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diff_file = sys.argv[1]
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try:
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with open(diff_file, 'r') as f:
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diff_text = f.read().strip()
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except FileNotFoundError:
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print(f"Error: File {diff_file} not found.")
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return
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else:
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# Get staged diff from git
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diff_text = get_staged_diff()
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if not diff_text:
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print("No staged changes found. Stage some changes with 'git add' first.")
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return
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if not diff_text:
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print("No diff content to process.")
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return
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# Generate and print commit message
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commit_message = generate_commit_message(diff_text)
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print(f"\nSuggested commit message:")
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print(f" {commit_message}")
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if __name__ == "__main__":
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main()
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```
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### Usage Examples
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1. **Generate from staged git changes:**
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```bash
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python generate_commit.py
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```
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2. **Generate from a diff file:**
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```bash
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python generate_commit.py my_changes.diff
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```
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3. **Use in your own code:**
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```python
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from generate_commit import generate_commit_message
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diff = """diff --git a/app.py b/app.py
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index e69de29..f4c3b4a 100644
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--- a/app.py
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+++ b/app.py
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@@ -0,0 +1,3 @@
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+def add(a, b):
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+ return a + b
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"""
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message = generate_commit_message(diff)
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print(message)
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```
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## Examples
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Input (user message content as formatted in training data):
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```
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Generate a concise and descriptive commit message for this git diff:
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```diff
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diff --git a/src/ossos-pipeline/scripts/update_astrometry.py b/src/ossos-pipeline/scripts/update_astrometry.py
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index <HASH>..<HASH> 100644
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--- a/src/ossos-pipeline/scripts/update_astrometry.py
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+++ b/src/ossos-pipeline/scripts/update_astrometry.py
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@@ -159,8 +159,11 @@ def recompute_mag(mpc_in):
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cutout = image_slice_downloader.download_cutout(reading, needs_apcor=True)
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cutout.zmag = new_zp
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+ if math.fabs(new_zp - old_zp) > 0.3:
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+ logging.warning("Large change in zeropoint detected: {} -> {}".format(old_zp, new_zp))
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try:
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- (x, y, mag, merr) = cutout.get_observed_magnitude(zmag=old_zp)
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+ (x, y, mag, merr) = cutout.get_observed_magnitude(zmag=new_zp)
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(x, y) = cutout.get_observed_coordinates((x, y))
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except:
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logging.warn("Failed to do photometry.")
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```
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```
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Possible outputs:
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- fix: use new_zp instead of old_zp for magnitude calculation and add zeropoint change warning
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- fix: correct zeropoint usage in photometry and add warning for large zeropoint changes
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- refactor: update magnitude calculation to use new zeropoint and add change detection
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## Training summary
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- Base model: `google/gemma-3-270m-it` (Gemma 3, 270M, instruction-tuned).
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- Method: LoRA fine-tuning with MLX (`mlx_lm lora`). Prompt masking was enabled so the model learns from the assistant response.
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+
- **Training data**: `data/train_gpt-oss-20b.jsonl` in this repository - a dataset converted to chat format with diffs fenced as ```diff and English, single-line commit messages as targets. This dataset is Python-focused.
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+
- Data format: Each training example uses the exact user prompt format shown above in the chat template structure.
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278 |
- Context/config highlights: max sequence length ~2048 tokens; diffs truncated to ~3,000 characters during preprocessing/inference to be model-friendly.
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+
- **Important**: To achieve best results, match the exact input format used in the training data.
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## Evaluation
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## License and credits
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- Base model: Google Gemma 3 (`google/gemma-3-270m-it`). Use subject to the Gemma license terms.
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+
- Fine-tuning code: MLX and utilities in this repository. See repository license for details.
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