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---
dataset_info:
features:
- name: model_type
dtype: string
- name: namespace
dtype: string
- name: model_name
dtype: string
- name: training_method
dtype: string
- name: model_size
dtype: int64
- name: trainable_params
dtype: int64
- name: url
dtype: string
- name: doi
dtype: float64
splits:
- name: train
num_bytes: 6257
num_examples: 40
download_size: 4879
dataset_size: 6257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
pretty_name: PEFT Unit Test Generation Experiments
size_categories:
- n<1K
---
# PEFT Unit Test Generation Experiments
## Dataset description
The **PEFT Unit Test Generation Experiments** dataset contains metadata and details about a set of trained models used for generating unit tests with parameter-efficient fine-tuning (PEFT) methods. This dataset includes models from multiple namespaces and various sizes, trained with different tuning methods to provide a comprehensive resource for unit test generation research.
## Dataset Structure
### Data Fields
Each example in the dataset corresponds to a specific trained model variant and includes the following features:
| Feature Name | Description |
|-------------------|-----------------------------------------------------------------------------------------------------|
| `model_type` | The type or architecture of the base model (e.g., codegen, starcoder). |
| `namespace` | The organization or group that created or published the base model (e.g., Salesforce, meta-llama). |
| `model_name` | The specific name or identifier of the model. |
| `training_method` | The parameter-efficient fine-tuning method used for training (e.g., full fine-tuning, LoRA, IA³). |
| `model_size` | The size of the model, typically measured in number of parameters (e.g., 350M, 7B). |
| `trainable_params`| The number of trainable parameters for the specific tuning method and [hyperparameters](#training-hyperparameters). |
| `url` | A direct link to the model repository. |
| `doi` | The digital object identifier associated with the trained model. |
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
### Training Hyperparameters
#### Model-agnostic Hyperparameters
<table>
<thead>
<tr>
<th>Hyperparameter</th>
<th>Method</th>
<th>Value</th>
</tr>
</thead>
<tbody>
<tr style="font-weight: bold;">
<td colspan="3">Common</td>
</tr>
<tr>
<td>Optimizer</td>
<td>-</td>
<td>AdamW</td>
</tr>
<tr>
<td>LR schedule</td>
<td>-</td>
<td>Linear</td>
</tr>
<tr>
<td>LR warmup ratio</td>
<td>-</td>
<td>0.1</td>
</tr>
<tr>
<td>Batch size</td>
<td>-</td>
<td>1</td>
</tr>
<tr>
<td>Gradient accumulation steps</td>
<td>-</td>
<td>8</td>
</tr>
<tr>
<td># Epochs</td>
<td>-</td>
<td>3</td>
</tr>
<tr>
<td>Precision</td>
<td>-</td>
<td>Mixed</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="4">Learning rate</td>
<td>Full fine-tuning</td>
<td>5E-5</td>
</tr>
<tr>
<td>LoRA</td>
<td>3E-4</td>
</tr>
<tr>
<td>(IA)<sup>3</sup></td>
<td>3E-4</td>
</tr>
<tr>
<td>Prompt tuning</td>
<td>3E-3</td>
</tr>
<tr style="font-weight: bold;">
<td colspan="3">Method specific</td>
</tr>
<tr>
<td>Alpha</td>
<td>LoRA</td>
<td>32</td>
</tr>
<tr>
<td>Dropout</td>
<td>LoRA</td>
<td>0.1</td>
</tr>
<tr>
<td>Rank</td>
<td>LoRA</td>
<td>16</td>
</tr>
<tr>
<td>Virtual tokens</td>
<td>Prompt tuning</td>
<td>20</td>
</tr>
</tbody>
</table>
#### Model-specific Hyperparameters
<table>
<thead>
<tr>
<th>Hyperparameter</th>
<th>Method</th>
<th>Model</th>
<th>Value</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="10" style="vertical-align: middle;">Targeted attention modules</td>
<td rowspan="10" style="vertical-align: middle;">LoRA, (IA)<sup>3</sup></td>
<td>codegen-350M-multi</td>
<td>qkv_proj</td>
</tr>
<tr><td>Salesforce/codegen2-1B_P</td><td>qkv_proj</td></tr>
<tr><td>Salesforce/codegen2-3_7B_P</td><td>qkv_proj</td></tr>
<tr><td>Salesforce/codegen2-7B_P</td><td>qkv_proj</td></tr>
<tr><td>Salesforce/codegen2-16B_P</td><td>qkv_proj</td></tr>
<tr><td>meta-llama/CodeLlama-7b-hf</td><td>q_proj, v_proj</td></tr>
<tr><td>bigcode/starcoderbase</td><td>c_attn</td></tr>
<tr><td>bigcode/starcoder2-3b</td><td>q_proj, v_proj</td></tr>
<tr><td>bigcode/starcoder2-7b</td><td>q_proj, v_proj</td></tr>
<tr><td>bigcode/starcoder2-15b</td><td>q_proj, v_proj</td></tr>
<tr>
<td rowspan="10" style="vertical-align: middle;">Targeted feedforward modules</td>
<td rowspan="10" style="vertical-align: middle;">(IA)<sup>3</sup></td>
<td>codegen-350M-multi</td>
<td>fc_out</td>
</tr>
<tr><td>Salesforce/codegen2-1B_P</td><td>fc_out</td></tr>
<tr><td>Salesforce/codegen2-3_7B_P</td><td>fc_out</td></tr>
<tr><td>Salesforce/codegen2-7B_P</td><td>fc_out</td></tr>
<tr><td>Salesforce/codegen2-16B_P</td><td>fc_out</td></tr>
<tr><td>meta-llama/CodeLlama-7b-hf</td><td>down_proj</td></tr>
<tr><td>bigcode/starcoderbase</td><td>mlp.c_proj</td></tr>
<tr><td>bigcode/starcoder2-3b</td><td>q_proj, c_proj</td></tr>
<tr><td>bigcode/starcoder2-7b</td><td>q_proj, c_proj</td></tr>
<tr><td>bigcode/starcoder2-15b</td><td>q_proj, c_proj</td></tr>
</tbody>
</table>
## Training Runs
|