---
library_name: transformers
tags: []
---

# Model Card for LargeCodeModelGPTBigCode

## Model Overview

```LargeCodeModelGPTBigCode``` is a model designed for code test generation and analysis. It is based on [GPTBigCode](https://huggingface.co/docs/transformers/main/en/model_doc/gpt_bigcode) and is specifically tailored for handling and generating tests for code. The model has been trained on a small manually labeled dataset of code and can be used for various tasks related to code analysis and testing.

<!-- Provide a quick summary of what the model is/does. -->

### Features:
* Code test generation.
* Python code analysis and generation.
* Uses a pre-trained GPT2 model integrated with Hugging Face.

## How it Works

The model is loaded from an external repository, such as Hugging Face, and is initialized using the ```class LargeCodeModelGPTBigCode```. Several parameters can be specified during initialization to configure the model, such as:

* gpt2_name: The link to the model on Hugging Face 
* prompt_string: An additional wrapper for better understanding of the task by the model 
* params_inference: Inference parameters (used in self.gpt2.generate(**inputs, **inference_params))
* max_length: The maximum number of tokens in the sequence 
* device: The device to run the model on 
* saved_model_path: Path to the fine-tuned model
* num_lines: Number of lines (due to "non-terminating" model generation)
* flag_hugging_face: Flag to enable usage with Hugging Face (default: False)
* flag_pretrained: Flag to initialize the model with pre-trained weights

You should download [inference_gptbigcode.py](inference_gptbigcode.py) for proper model usage or use ```git clone https://huggingface.co/4ervonec19/SimpleTestGenerator``` instead. Also you may use this file for inference parameters tuning.

### Model Initialization

```python
from inference_gptbigcode import LargeCodeModelGPTBigCode

gpt2bigcode = "4ervonec19/SimpleTestGenerator"

CodeModel = LargeCodeModelGPTBigCode(gpt2_name=gpt2bigcode, 
                                    flag_pretrained=True, 
                                    flag_hugging_face=True)

```

### Inference Example

Here’s an example of inference where the model is used to generate tests based on a given code snippet:

```python
code_example = '''def equals_zero(a):
    if a == 0:
      return True
    return False'''

tests_generated = CodeModel.input_inference(code_text=code_example)

# Result
print(tests_generated['generated_output'])
```

### Output:
The result will contain the input function and generated tests dict, for example:

```python
{'input_function': ('def equals_zero(a):\n    if a == 0:\n      return True\n    return False',),
 'generated_output': 'def test_equals_zero():\n    assert equals_zero(0) is True\n    assert equals_zero(1) is False\n    assert equals_zero(0) is True\n    assert equals_zero(1.5) is False'}
```

## Model Details

* Architecture: GPT2
* Pretraining: Yes, the model uses a pre-trained GPT2 version for test generation and code generation.
* Framework: PyTorch/HuggingFace
* License: MIT (or another, depending on the model's license)

## Limitations

* The model may not always generate correct or optimal tests, especially for complex or non-standard code fragments.
* Some understanding of code structure may be required for optimal results.
* The quality of generated tests depends on the quality of the input code and its context.