metadata
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. |
url |
A direct link to the model repository. |
doi |
The digital object identifier associated with the trained model. |
Dataset Details
Dataset Description
Training Hyperparameters
Model-agnostic Hyperparameters
| Hyperparameter | Method | Value |
|---|---|---|
| Common | ||
| Optimizer | - | AdamW |
| LR schedule | - | Linear |
| LR warmup ratio | - | 0.1 |
| Batch size | - | 1 |
| Gradient accumulation steps | - | 8 |
| # Epochs | - | 3 |
| Precision | - | Mixed |
| Learning rate | Full fine-tuning | 5E-5 |
| LoRA | 3E-4 | |
| (IA)3 | 3E-4 | |
| Prompt tuning | 3E-3 | |
| Method specific | ||
| Alpha | LoRA | 32 |
| Dropout | LoRA | 0.1 |
| Rank | LoRA | 16 |
| Virtual tokens | Prompt tuning | 20 |
Model-specific Hyperparameters
| Hyperparameter | Method | Model | Value |
|---|---|---|---|
| Targeted attention modules | LoRA, (IA)3 | codegen-350M-multi | qkv_proj |
| Salesforce/codegen2-1B_P | qkv_proj | ||
| Salesforce/codegen2-3_7B_P | qkv_proj | ||
| Salesforce/codegen2-7B_P | qkv_proj | ||
| Salesforce/codegen2-16B_P | qkv_proj | ||
| meta-llama/CodeLlama-7b-hf | q_proj, v_proj | ||
| bigcode/starcoderbase | c_attn | ||
| bigcode/starcoder2-3b | q_proj, v_proj | ||
| bigcode/starcoder2-7b | q_proj, v_proj | ||
| bigcode/starcoder2-15b | q_proj, v_proj | ||
| Targeted feedforward modules | (IA)3 | codegen-350M-multi | fc_out |
| Salesforce/codegen2-1B_P | fc_out | ||
| Salesforce/codegen2-3_7B_P | fc_out | ||
| Salesforce/codegen2-7B_P | fc_out | ||
| Salesforce/codegen2-16B_P | fc_out | ||
| meta-llama/CodeLlama-7b-hf | down_proj | ||
| bigcode/starcoderbase | mlp.c_proj | ||
| bigcode/starcoder2-3b | q_proj, c_proj | ||
| bigcode/starcoder2-7b | q_proj, c_proj | ||
| bigcode/starcoder2-15b | q_proj, c_proj |
Training Runs
