rhaymison/gemma-portuguese-luana-2b

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2.6BQuant:BF16Ctx Length:8kPublished:Mar 25, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

The rhaymison/gemma-portuguese-luana-2b is a Gemma-based model developed by rhaymison, fine-tuned specifically for Portuguese language understanding and text generation. This 2 billion parameter model was trained on a superset dataset with 250,000 instructions, focusing on generalist text generation and instruction following in Portuguese. It is not optimized for mathematical or coding tasks, but excels at processing Portuguese inferences, making it suitable for applications requiring strong Portuguese language capabilities.

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Overview

The rhaymison/gemma-portuguese-luana-2b model is a Gemma-based language model developed by rhaymison, specifically fine-tuned for the Portuguese language. Updated on 2024-04-10, this model was trained using a superset dataset comprising 250,000 instructions, with a primary focus on enhancing Portuguese text generation and instruction-following capabilities.

Key Capabilities

  • Portuguese Language Proficiency: The model is a generalist, designed to understand and generate text with a strong focus on Portuguese inferences.
  • Text Generation: Excels in generating coherent and contextually relevant text in Portuguese.
  • Instruction Following: Capable of processing and responding to instructions provided in Portuguese.
  • Adaptability: Its fine-tuning for Portuguese makes it a suitable base for further specialization in specific domains within the language.

Limitations

  • No Math or Code Training: The model was explicitly not trained on mathematical or coding tasks, and therefore, its performance in these areas is not optimized.

Performance Metrics

Evaluated on the Open Portuguese LLM Leaderboard, the model achieved an Average score of 34.94. Notable scores include:

  • Assin2 RTE: 70.86
  • PT Hate Speech Binary: 51.83
  • FaQuAD NLI: 43.97

Good For

  • Applications requiring robust Portuguese text generation.
  • Instruction-based tasks in Portuguese.
  • As a foundational model for further fine-tuning on specific Portuguese-language datasets or domains.

Finetunes
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