dimivelev/gemma-4-e4b-it-unsloth-bnb-4bit-latex-ocr-full
The dimivelev/gemma-4-e4b-it-unsloth-bnb-4bit-latex-ocr-full is a 7.9 billion parameter instruction-tuned language model, fine-tuned by dimivelev from unsloth/gemma-4-e4b-it-unsloth-bnb-4bit. This model leverages Unsloth and Huggingface's TRL library for 2x faster training, making it efficient for deployment. With a context length of 32768 tokens, it is optimized for tasks requiring processing of longer inputs, particularly those related to LaTeX OCR.
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Overview
This model, dimivelev/gemma-4-e4b-it-unsloth-bnb-4bit-latex-ocr-full, is an instruction-tuned variant of the Gemma 4 architecture, developed by dimivelev. It is built upon the unsloth/gemma-4-e4b-it-unsloth-bnb-4bit base model and has been fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library. A key characteristic of this model is its optimized training process, which was achieved approximately 2x faster due to the Unsloth framework.
Key Capabilities
- Efficient Fine-tuning: Benefits from Unsloth's optimizations for faster training.
- Instruction-tuned: Designed to follow instructions effectively for various tasks.
- Large Context Window: Supports a context length of 32768 tokens, suitable for processing extensive documents or conversations.
Good for
- Applications requiring a Gemma 4-based model with enhanced training efficiency.
- Tasks that benefit from a large context window, such as document analysis or summarization.
- Use cases where rapid fine-tuning and deployment are critical.