ngoan/Llama-2-7b-vietnamese-20k

Model Card for Llama 2 Fine-Tuned on Vietnamese Instructions

Model Details

• —Model Name: Llama-2-7b-vietnamese-20k
• —Architecture: Llama 2 7B
• —Fine-tuning Data Size: 20,000 instruction samples
• —Purpose: To demonstrate the performance of the Llama 2 model on Vietnamese and gather initial insights. A more comprehensive model and evaluation will be released soon.
• —Availability: The model checkpoint can be accessed on Hugging Face: ngoantech/Llama-2-7b-vietnamese-20k

Intended Use

This model is intended for researchers, developers, and enthusiasts who are interested in understanding the performance of the Llama 2 model on Vietnamese. It can be used for generating Vietnamese text based on given instructions or for any other task that requires a Vietnamese language model.

Example Output

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Limitations

• —Data Size: The model was fine-tuned on a relatively small dataset of 20,000 instruction samples, which might not capture the full complexity and nuances of the Vietnamese language.
• —Preliminary Model: This is an initial experiment with the Llama 2 architecture on Vietnamese. More refined versions and evaluations will be available soon.
• —Performance: Specific performance metrics on this fine-tuned model will be provided in the upcoming comprehensive evaluation.

Ethical Considerations

• —Bias and Fairness: Like any other machine learning model, there is a possibility that this model might reproduce or amplify biases present in the training data.
• —Use in Critical Systems: As this is a preliminary model, it is recommended not to use it for mission-critical applications without proper validation.
• —Fine-tuning Data: The model was fine-tuned on a custom dataset of 20,000 instruction samples in Vietnamese. More details about the composition and source of this dataset will be provided in the detailed evaluation report.

Credits

I would like to express our gratitude to the creators of the Llama 2 architecture and the Hugging Face community for their tools and resources.

Contact

ngoantech@gmail.com

https://github.com/ngoanpv