artificialguybr/llama3-8b-alpacadata-ptbr
The artificialguybr/llama3-8b-alpacadata-ptbr is an 8 billion parameter Llama 3-based causal language model, fine-tuned by artificialguybr using the NousResearch/Meta-Llama-3-8B base model. It is specifically optimized for Portuguese language understanding and generation, trained on a cleaned and translated version of the Alpaca dataset (dominguesm/alpaca-data-pt-br). This model excels at generating responses to natural language questions and prompts in Portuguese, making it suitable for applications requiring deep understanding of the Brazilian Portuguese language.
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Model Overview
The artificialguybr/llama3-8b-alpacadata-ptbr is an 8 billion parameter Llama 3-based language model, fine-tuned from the NousResearch/Meta-Llama-3-8B base model. Its primary distinction lies in its specialization for the Portuguese language, specifically Brazilian Portuguese, achieved through fine-tuning on the dominguesm/alpaca-data-pt-br dataset.
Key Capabilities
- Portuguese Language Understanding: Designed to deeply understand and process natural language in Portuguese.
- Response Generation: Capable of generating relevant and coherent responses to a wide range of questions and prompts.
- Culturally Relevant: Trained on a dataset translated and cleaned for the Brazilian market, ensuring linguistic and cultural relevance.
Training Details
The model was trained on a dataset of 51,000 examples, which is a carefully reviewed and translated version of the original Stanford Alpaca Dataset. This process focused on fixing issues and enhancing data quality. Training hyperparameters included a learning rate of 2e-05, 2 epochs, and the use of flash_attention for efficiency. The model achieved a validation loss of 1.1227.
Intended Uses
- Chatbots and Virtual Assistants: Ideal for conversational AI applications requiring Portuguese language interaction.
- Language Translation Systems: Can enhance machine translation models by providing robust Portuguese language generation.
- Brazilian Market Applications: Provides a specialized resource for applications targeting the Brazilian market.
Limitations
Users should note that the model may not generalize well to other languages or dialects, and its performance might be limited on out-of-domain topics or prompts requiring common sense not present in its training data.