Hi, I’m Seniru Epasinghe 👋

I’m an AI undergraduate and an AI enthusiast, working on machine learning projects and open-source contributions.
I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier.

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Finetuned-qwen2.5-coder-0.5B model on 100000 rows of a cutom dataset containing. git-differences and respective commit messages

• dataset huggingface link
• dataset kaggle link

Each row of the dataset was formatted as below to suit finetuning requirement of Qwen2.5-coder model so we have to use the same prompt for better results

"""Generate a concise and meaningful commit message based on the provided Git diff.

### Git Diff:
{Git diff from dataset}

### Commit Message:"""

Code for inference of the gguf model is given below

from llama_cpp import Llama

modelGGUF = Llama.from_pretrained(
    repo_id="seniruk/qwen2.5coder-0.5B_commit_msg",
    filename="qwen0.5-finetuned.gguf",
    rope_scaling={"type": "linear", "factor": 2.0},
    chat_format=None,  # Disables any chat formatting
    n_ctx=32768,  # Set the context size explicitly
)

# Define the commit message prompt (Minimal format, avoids assistant behavior)
commit_prompt = """Generate a meaningful commit message explaining all the changes in the provided Git diff.

### Git Diff:
{}

### Commit Message:"""  # Removed {} after "Commit Message:" to prevent pre-filled text.

# Git diff example for commit message generation
git_diff_example = """
diff --git a/index.html b/index.html
index 89abcde..f123456 100644
--- a/index.html
+++ b/index.html
@@ -5,16 +5,6 @@ <body>
     <h1>Welcome to My Page</h1>

-    <table border="1">
-        <tr>
-            <th>Name</th>
-            <th>Age</th>
-        </tr>
-        <tr>
-            <td>John Doe</td>
-            <td>30</td>
-        </tr>
-    </table>

+    <p>This is a newly added paragraph replacing the table.</p>
 </body>
</html>
"""

# Prepare the raw input prompt
input_prompt = commit_prompt.format(git_diff_example)

# Generate commit message
output = modelGGUF(
    input_prompt,
    max_tokens=64,
    temperature=0.6, # Balanced randomness
    top_p=0.8,      # Controls nucleus sampling
    top_k=50,       # Limits vocabulary selection
)

# Decode and print the output
commit_message = output["choices"][0]["text"].strip()

print("\nGenerated Commit Message:\n{}".format(commit_message))