ping98k/gemma-7b-translator-0.3

TEXT GENERATIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:8.5BQuant:FP8Context Size:8kPublished:Apr 26, 2024Architecture:Transformer Featherless Exclusive Cold

The ping98k/gemma-7b-translator-0.3 is an 8.5 billion parameter Gemma-based model specifically fine-tuned for translation tasks, particularly between English and Thai. It is designed to process up to 8192 tokens and specializes in converting text snippets while adhering to a specific input/output format. This model's primary differentiator is its focused application on structured translation, aiming for accurate language conversion within defined tags.

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Model Overview

The ping98k/gemma-7b-translator-0.3 is an 8.5 billion parameter language model built upon the Gemma architecture, fine-tuned for translation. Its core function is to translate text between English and Thai, operating within a defined input/output structure. The model supports a context length of up to 8192 tokens, making it suitable for translating moderately sized text segments.

Key Capabilities

  • Specialized Translation: Primarily designed for English-Thai translation, focusing on accuracy within its domain.
  • Structured Input/Output: Expects and produces output in a specific format, including <original> and <translate to="lang"> tags, which helps in integrating it into automated translation pipelines.
  • Gemma-based Architecture: Leverages the capabilities of the Gemma family of models, known for their performance in various language tasks.

Limitations and Considerations

  • Format Adherence: The model's performance is highly dependent on strict adherence to the specified input format. Deviations can lead to incorrect or malformed outputs.
  • Output Anomalies: The README indicates that the model may occasionally fail to follow the prescribed format, sometimes inserting unexpected HTML tags like </input> or producing other malformed responses.
  • Specific Use Case: This model is highly specialized for translation and the described format; its utility for general-purpose language tasks is not the primary focus.

Good for

  • Developers requiring a dedicated English-Thai translation model that operates with a structured input/output format.
  • Applications where the translation task can be precisely framed within the model's expected tag-based format.
  • Experimentation with fine-tuned Gemma models for specific language pairs and structured data processing.

Finetunes
2 models