Ba2han/gemma-e2b-TR-OCR

VISIONConcurrent Unit Cost:1Model Size:5.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 1, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Ba2han/gemma-e2b-TR-OCR is a 5.1 billion parameter language model developed by Ba2han, finetuned from unsloth/gemma-4-E2B-it. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for specific tasks related to OCR (Optical Character Recognition) and Turkish language processing, leveraging its Gemma-based architecture.

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Model Overview

Ba2han/gemma-e2b-TR-OCR is a 5.1 billion parameter language model developed by Ba2han. It is finetuned from the unsloth/gemma-4-E2B-it base model, indicating a specialization or adaptation from an existing Gemma variant. The model was trained with a focus on efficiency, utilizing the Unsloth library in conjunction with Huggingface's TRL library, which enabled a reported 2x faster training process.

Key Characteristics

  • Base Model: Finetuned from unsloth/gemma-4-E2B-it.
  • Parameter Count: 5.1 billion parameters.
  • Training Efficiency: Leverages Unsloth for accelerated training, achieving 2x faster speeds.
  • License: Distributed under the Apache-2.0 license.

Potential Use Cases

Given its finetuning from a Gemma-E2B model and the 'TR-OCR' suffix, this model is likely optimized for:

  • Optical Character Recognition (OCR): Tasks involving text extraction from images or documents.
  • Turkish Language Processing: Applications requiring understanding or generation of Turkish text, potentially in conjunction with OCR outputs.

This model offers a specialized solution for developers working on OCR and Turkish language-specific tasks, benefiting from the performance and efficiency gains provided by the Unsloth training framework.

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