amadeusai/Amadeus-Verbo-MI-Qwen-2.5-1.5B-PT-BR-Instruct-Experimental

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Mar 26, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The amadeusai/Amadeus-Verbo-MI-Qwen-2.5-1.5B-PT-BR-Instruct-Experimental is a 1.5 billion parameter instruction-tuned causal language model, merged from amadeusai/AV-BI-Qwen2.5-1.5B-PT-BR-Instruct and Qwen/Qwen2.5-1.5B-Instruct using the SLERP method. This model is specifically optimized for instruction following in Portuguese, leveraging a 32768 token context length. Its primary strength lies in generating responses to prompts in Brazilian Portuguese, making it suitable for applications requiring localized language understanding and generation.

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Model Overview

The amadeusai/Amadeus-Verbo-MI-Qwen-2.5-1.5B-PT-BR-Instruct-Experimental is a 1.5 billion parameter instruction-tuned language model, developed by amadeusai. It was created by merging two base models: amadeusai/AV-BI-Qwen2.5-1.5B-PT-BR-Instruct and Qwen/Qwen2.5-1.5B-Instruct, utilizing the SLERP (Spherical Linear Interpolation) merge method. This approach combines the strengths of both models, aiming to enhance performance, particularly for Portuguese language tasks.

Key Capabilities

  • Instruction Following: Designed to understand and execute instructions effectively.
  • Portuguese Language Focus: Optimized for generating high-quality text in Brazilian Portuguese.
  • Causal Language Modeling: Capable of predicting the next token in a sequence, enabling coherent and contextually relevant text generation.
  • Qwen2.5 Architecture: Built upon the Qwen2.5 model family, known for its robust performance.
  • Extended Context Window: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining conversational history.

When to Use This Model

This model is particularly well-suited for applications requiring:

  • Portuguese-centric AI applications: Ideal for chatbots, content generation, and virtual assistants operating in Brazilian Portuguese.
  • Instruction-based tasks: Excels at following specific commands and generating structured outputs.
  • Resource-efficient deployment: With 1.5 billion parameters, it offers a balance between performance and computational cost, making it suitable for environments where larger models might be prohibitive.

For more technical details, refer to the associated research paper: Amadeus-Verbo Technical Report.

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