rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Apr 30, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

The rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct is an 8 billion parameter Llama 3-based instruction-tuned model developed by rhaymison, specifically fine-tuned for chat in Portuguese. Trained on a superset of 300,000 Portuguese chat conversations, this model aims to address the scarcity of high-quality Portuguese language models. It excels in conversational tasks and general Portuguese language understanding, offering enhanced compatibility with GGUF versions available for LlamaCpp.

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Model Overview

The rhaymison/Llama-3-portuguese-Tom-cat-8b-instruct is an 8 billion parameter instruction-tuned model based on the Llama 3 architecture, developed by rhaymison. Its primary goal is to fill the gap in high-quality Portuguese language models, having been extensively fine-tuned on a superset of 300,000 Portuguese chat conversations. This specialization makes it particularly adept at handling conversational tasks and understanding nuances of the Portuguese language.

Key Capabilities

  • Portuguese Language Proficiency: Optimized for chat and general language understanding in Portuguese.
  • Instruction Following: Designed to follow instructions effectively, especially in conversational contexts.
  • Quantization Support: Supports 4-bit and 8-bit quantization for efficient deployment on less powerful hardware (e.g., T4 or V100 GPUs), with full model requiring A100.
  • GGUF Compatibility: A GGUF family of this model (e.g., Llama-3-portuguese-Tom-cat-8b-instruct-q8-gguf) is available for use with LlamaCpp.

Performance Highlights

Evaluated on the Open Portuguese LLM Leaderboard, the model achieved an Average score of 70.57. Notable scores include:

  • Assin2 RTE: 90.91
  • HateBR Binary: 86.99
  • FaQuAD NLI: 76.05
  • OAB Exams: 51.07

Ideal Use Cases

  • Portuguese Chatbots: Excellent for building conversational AI agents that interact in Portuguese.
  • Portuguese Content Generation: Suitable for generating text, answering questions, and summarizing information in Portuguese.
  • Educational Applications: Can act as a tutor or provide explanations in Portuguese, as demonstrated by its ability to explain mathematical concepts.
  • Resource-Constrained Environments: Quantized versions allow for deployment on hardware with limited memory.

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