README.md

---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
datasets:
- jtatman/python-code-dataset-500k
license: mit
---

## Citation 

```bibtex
@misc{Molino2019,
  author = {Piero Molino and Yaroslav Dudin and Sai Sumanth Miryala},
  title = {Ludwig: a type-based declarative deep learning toolbox},
  year = {2019},
  eprint = {arXiv:1909.07930},
}
```
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
# Model Card for Model ID

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





## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model focuses on fine-tuning the Llama-2 7B large language model for Python code generation. The project leverages Ludwig, an open-source toolkit, and a dataset of 500k Python code samples from Hugging Face. The model applies techniques such as prompt templating, zero-shot inference, and few-shot learning, enhancing the model's performance in generating Python code snippets efficiently.

- **Developed by:** Kevin Geejo, Aniket Yadav, Rishab Pandey  

- **Model type:** Fine-tuned Llama-2 7B for Python code generation  
- **Language(s) (NLP):** Python (for code generation tasks)  
- **License:** Not explicitly mentioned, but Llama-2 models are typically governed by Meta AI’s open-source licensing  
- **Finetuned from model [optional]:** Llama-2 7B (Meta AI, 2023)  

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** Hugging Face


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

- Python code generation for software development  
- Automation of coding tasks  
- Developer productivity enhancement  

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

- Code completion, bug fixing, and Python code translation  

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

- Non-Python programming tasks  
- Generation of sensitive, legal, or medical content  

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

- Limited to Python programming tasks  
- Dataset biases from Hugging Face's Python Code Dataset  
- Environmental impact from computational costs during fine-tuning  

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be aware of computational efficiency trade-offs and potential limitations in generalizing to new Python tasks.  

## How to Get Started with the Model

Use the code below to get started with the model:

```python
# Example setup (simplified)
import ludwig
from transformers import AutoModel

model = AutoModel.from_pretrained("llama-2-7b-python")
```  

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

- 500k Python code samples sourced from Hugging Face  

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

- **Preprocessing [optional]:** Hugging Face Python Code Dataset  
- **Training regime:** Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA)  

#### Speeds, Sizes, Times [optional]

- Not explicitly mentioned in the document  

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

- Derived from Python code datasets on Hugging Face  

#### Factors

- Python code generation tasks  

#### Metrics

- Code correctness and efficiency  

### Results

- Fine-tuning improved Python code generation performance  

#### Summary

The fine-tuned model showed enhanced proficiency in generating Python code snippets, reflecting its adaptability to specific coding tasks.  

## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]  

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).  





### Model Architecture and Objective

- Llama-2 7B model architecture fine-tuned for Python code generation  

### Compute Infrastructure

- Not explicitly mentioned  

#### Hardware

- Not specified  

#### Software

- Ludwig toolkit and Hugging Face integration  

**BibTeX:**  

[More Information Needed]  

**APA:**  

[More Information Needed]  

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

- **Llama-2:** Open-source large language model by Meta AI  
- **LoRA (Low-Rank Adaptation):** Efficient fine-tuning method modifying fewer model parameters  
- **PEFT:** Parameter-efficient fine-tuning technique  

## More Information [optional]

[More Information Needed]  

## Model Card Authors [optional]

Kevin Geejo, Aniket Yadav, Rishab Pandey  

## Model Card Contact

[email protected], [email protected], [email protected]  

### Framework versions

- **Llama-2 version:** 7B  
- **Ludwig version:** 0.8  
- **Hugging Face integration:** Latest