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Merge pull request #937 from KennyDizi/main

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Optimize document table format
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2024-06-01 10:53:47 +03:00
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@ -11,144 +11,33 @@ Here are the results:
<br>
<br>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
td, th {
font-size: 16px; /* Adjust this value to your preference */
}
table {
width: 100%;
border-collapse: collapse;
}
th {
background-color: #f2f2f2;
border: 1px solid #dddddd;
text-align: center;
padding: 8px;
}
td {
border: 1px solid #dddddd;
text-align: center;
padding: 8px;
}
tr:nth-child(even) {
background-color: #f9f9f9;
text-align: center;
}
</style>
<title>Model Performance Table</title>
</head>
<body>
**Model performance:**
<table>
<tr>
<th align="center">Model name</th>
<th align="center">Model size [B]</th>
<th align="center">Better than gpt-4 rate, after fine-tuning [%]</th>
</tr>
<tr>
<td align="center"><b>DeepSeek 34B-instruct</b></td>
<td align="center"><b>34</b></td>
<td align="center"><b>40.7</b></td>
</tr>
<tr>
<td align="center">DeepSeek 34B-base</td>
<td align="center">34</td>
<td align="center">38.2</td>
</tr>
<tr>
<td align="center">Phind-34b</td>
<td align="center">34</td>
<td align="center">38</td>
</tr>
<tr>
<td align="center">Granite-34B</td>
<td align="center">34</td>
<td align="center">37.6</td>
</tr>
<tr>
<td align="center">Codestral-22B-v0.1</td>
<td align="center">22</td>
<td align="center">32.7</td>
</tr>
<tr>
<td align="center">QWEN-1.5-32B</td>
<td align="center">32</td>
<td align="center">29</td>
</tr>
<tr>
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
</tr>
<tr>
<td align="center"><b>CodeQwen1.5-7B</b></td>
<td align="center"><b>7</b></td>
<td align="center"><b>35.4</b></td>
</tr>
<tr>
<td align="center">Granite-8b-code-instruct</td>
<td align="center">8</td>
<td align="center">34.2</td>
</tr>
<tr>
<td align="center">CodeLlama-7b-hf</td>
<td align="center">7</td>
<td align="center">31.8</td>
</tr>
<tr>
<td align="center">Gemma-7B</td>
<td align="center">7</td>
<td align="center">27.2</td>
</tr>
<tr>
<td align="center">DeepSeek coder-7b-instruct</td>
<td align="center">7</td>
<td align="center">26.8</td>
</tr>
<tr>
<td align="center">Llama-3-8B-Instruct</td>
<td align="center">8</td>
<td align="center">26.8</td>
</tr>
<tr>
<td align="center">Mistral-7B-v0.1</td>
<td align="center">7</td>
<td align="center">16.1</td>
</tr>
</table>
</body>
| Model name | Model size [B] | Better than gpt-4 rate, after fine-tuning [%] |
|-----------------------------|----------------|----------------------------------------------|
| **DeepSeek 34B-instruct** | **34** | **40.7** |
| DeepSeek 34B-base | 34 | 38.2 |
| Phind-34b | 34 | 38 |
| Granite-34B | 34 | 37.6 |
| Codestral-22B-v0.1 | 22 | 32.7 |
| QWEN-1.5-32B | 32 | 29 |
| | | |
| **CodeQwen1.5-7B** | **7** | **35.4** |
| Granite-8b-code-instruct | 8 | 34.2 |
| CodeLlama-7b-hf | 7 | 31.8 |
| Gemma-7B | 7 | 27.2 |
| DeepSeek coder-7b-instruct | 7 | 26.8 |
| Llama-3-8B-Instruct | 8 | 26.8 |
| Mistral-7B-v0.1 | 7 | 16.1 |
<br>
**Fine-tuning impact:**
<body>
<table>
<tr>
<th align="center">Model name</th>
<th>Model size [B]</th>
<th>Fine-tuned</th>
<th>Better than gpt-4 rate [%]</th>
</tr>
<tr>
<td align="center">DeepSeek 34B-instruct</td>
<td align="center">34</td>
<td align="center">yes</td>
<td align="center">40.7</td>
</tr>
<tr>
<td align="center">DeepSeek 34B-instruct</td>
<td align="center">34</td>
<td align="center">no</td>
<td align="center">3.6</td>
</tr>
</table>
</body>
| Model name | Model size [B] | Fine-tuned | Better than gpt-4 rate [%] |
|---------------------------|----------------|------------|----------------------------|
| DeepSeek 34B-instruct | 34 | yes | 40.7 |
| DeepSeek 34B-instruct | 34 | no | 3.6 |
## Results analysis
@ -159,7 +48,6 @@ Here are the results:
- **The best small model** - For small 7B code-dedicated models, the gaps when fine-tuning are much larger. **CodeQWEN 1.5-7B** is by far the best model for fine-tuning.
- **Base vs. instruct** - For the top model (deepseek), we saw small advantage when starting from the instruct version. However, we recommend testing both versions on each specific task, as the base model is generally considered more suitable for fine-tuning.
## The dataset
### Training dataset
@ -171,11 +59,13 @@ On the raw data collected, we employed various automatic and manual cleaning tec
An example input prompt can be found [here](https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/settings/pr_code_suggestions_prompts.toml), and an example output can be found [here](https://github.com/Codium-ai/pr-agent/pull/910#issuecomment-2118761309).
### Evaluation dataset
- For each tool, we aggregated 100 additional examples to be used for evaluation. These examples were not used in the training dataset, and were manually selected to represent diverse real-world use-cases.
- For each test example, we generated two responses: one from the fine-tuned model, and one from the best code model in the world, `gpt-4-turbo-2024-04-09`.
- We used a third LLM to judge which response better answers the prompt, and will likely be perceived by a human as better response.
<br>
We experimented with three model as judges: `gpt-4-turbo-2024-04-09`, `gpt-4o`, and `claude-3-opus-20240229`. All three produced similar results, with the same ranking order. This strengthens the validity of our testing protocol.
Here is an example for a judge model feedback: