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---
datasets:
- flytech/python-codes-25k
language:
- en
base_model:
- meta-llama/Llama-3.1-8B
# Model Card for LLaMA 3.1 8B Model (Finetuned using Unsloth)
## Model Summary
This model is a finetuned version of LLaMA 3.1 8B, specifically adapted using the **Unsloth** dataset for enhanced performance on code generation tasks. The model excels in generating Python code and providing intelligent suggestions for developers. It has been trained to improve predictive accuracy and usability in AI-driven coding assistants.
## Model Details
### Model Description
This model is finetuned from LLaMA 3.1 8B, focusing on improving performance for code-related tasks. Using the Unsloth dataset, it has been trained to offer high-quality completions and optimize development workflows for Python programming.
- **Developed by:** Hisham
- **Model type:** Transformer-based Language Model (LLaMA 3.1 variant)
- **Language(s) (NLP):** Primarily English, Python-specific vocabulary
- **Finetuned from model:** LLaMA 3.1 8B
## Uses
### Direct Use
This model can be used directly to assist in code completion, suggestions, and problem-solving for Python-based projects. It provides smart completions based on the context of code.
### Downstream Use
The model can be fine-tuned further for specific software development tasks, including automation, refactoring, and debugging assistance.
### Out-of-Scope Use
The model is not suitable for non-code-related text generation or for use in domains requiring high factual precision, such as medical or legal documentation.
## Bias, Risks, and Limitations
Due to the nature of language models, this model may generate incorrect or incomplete code. There are also risks of biases in the code patterns it generates, potentially reproducing biased or unsafe coding practices present in the training data.
### Recommendations
Users (both direct and downstream) should be aware of the risks, biases, and limitations of the model. It is recommended to thoroughly test the generated code in critical systems and ensure compliance with best coding practices.
## How to Get Started with the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("hisham1404/llama3.1-python-coder")
# Example usage
inputs = tokenizer("def my_function():", return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
The model was finetuned using **Unsloth** framework.
#### Summary
The finetuned model demonstrates strong performance in generating usable Python code but may occasionally require human oversight to ensure code correctness and adherence to best practices.
## Model Examination
No specific interpretability work has been conducted on this model.
## Technical Specifications
### Model Architecture and Objective
The model is based on the LLaMA 3.1 8B architecture, which employs multi-head self-attention layers for natural language processing tasks. This variant has been optimized for code generation tasks.
#### Software
- **Frameworks:** PyTorch 2.0
- **Libraries:** Hugging Face Transformers
## Citation [optional]
**BibTeX:**
```bibtex
@misc{hisham2024llama3.1,
author = {Hisham},
title = {Finetuning LLaMA 3.1 8B with Unsloth for Python Code Generation},
year = {2024},
}
```
**APA:**
Hisham. (2024). *Finetuning LLaMA 3.1 8B with Unsloth for Python Code Generation*.
## More Information
For more details on fine-tuning, check out the LLaMA 3.1 model card and Hugging Face documentation.
## Model Card Authors
Model card authored by Hisham.
Let me know if you'd like any changes or additional details! |