Create README.md

README.md

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+---
+tags:
+- text-generation
+- llama-2
+- llama-2-7B
+- llama2-vietnamese
+- vietnamese
+---
+# Model Card for Llama 2 Fine-Tuned on Vietnamese Instructions
+
+## Model Details
+- Model Name: Llama-2-7b-vi-sample
+- 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-vi-sample
+
+## 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.
+
+## 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.
+
+## Usage
+To use this model via the Hugging Face API:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("ngoantech/Llama-2-7b-vi-sample")
+model = AutoModelForSeq2SeqLM.from_pretrained("ngoantech/Llama-2-7b-vi-sample")
+
+inputs = tokenizer.encode("YOUR INSTRUCTION HERE", return_tensors="pt")
+outputs = model.generate(inputs)
+decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(decoded)
+```
+
+## 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.