artificialguybr
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README.md
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wandb_project: meta-llama-8b-
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# out-llama8b-alpaca-data-pt-br
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This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the
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It achieves the following results on the evaluation set:
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- Loss: 1.1227
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## Model description
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##
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## Training procedure
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: meta-llama-8b-alpacadata-br
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wandb_entity:
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wandb_watch:
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# out-llama8b-alpaca-data-pt-br
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This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the [dominguesm/alpaca-data-pt-br](https://huggingface.co/dominguesm/alpaca-data-pt-br) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1227
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## Model description
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The model is a Portuguese language understanding model designed to generate responses to a wide range of questions and prompts. It takes as input a natural language question or prompt and outputs a corresponding response.
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The model is trained on a dataset of 51k examples, which is a cleaned and translated version of the original Alpaca Dataset released by Stanford. The original dataset was translated to Portuguese (Brazil) to provide a more culturally and linguistically relevant resource for the Brazilian market.
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The dataset was carefully reviewed to identify and fix issues present in the original release, ensuring that the model is trained on high-quality data. The model is intended to be used in applications where a deep understanding of Portuguese language is required, such as chatbots, virtual assistants, and language translation systems.
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## Intended uses:
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Generating responses to natural language questions and prompts in Portuguese
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Supporting chatbots, virtual assistants, and other conversational AI applications
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Enhancing language translation systems and machine translation models
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Providing a culturally and linguistically relevant resource for the Brazilian market
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## Limitations
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The model may not generalize well to other languages or dialects
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The model may not perform well on out-of-domain or unseen topics
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The model may not be able to handle ambiguous or open-ended prompts
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The model may not be able to understand nuances of regional dialects or slang
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The model may not be able to handle prompts that require common sense or real-world knowledge
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## Training procedure
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