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- ---
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- library_name: transformers
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- tags:
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- - rag
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- - security
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- - legal
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- - ai4good
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- license: apache-2.0
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- language:
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- - en
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- metrics:
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- - accuracy
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- base_model:
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- - google/gemma-3-4b-it
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- pipeline_tag: text-generation
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- ---
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  # GEMMA Document Rewriter for RAG Pipeline
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  ## Overview
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- The **GEMMA Document Rewriter for RAG Pipeline** is a state-of-the-art text rewriting model built on top of the pre-trained [Google Gemma 3 4B](https://huggingface.co/unsloth/gemma-3-4b-it) language model. This model has been fine-tuned using a LoRA (Low-Rank Adaptation) technique, with the adapter weights provided by [venkycs/ggemma-3-writer-lora](https://huggingface.co/venkycs/ggemma-3-writer-lora). The primary goal of this model is to intelligently rewrite documents by eliminating unnecessary information, byte spaces, and redundant content. It extracts and emphasizes the information that is significant for Retrieval-Augmented Generation (RAG) pipelines, outputting a clean, structured version of the document in Markdown format with appropriate headings.
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  ## Key Features
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+ ---
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+ library_name: transformers
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+ tags:
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+ - rag
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+ - security
6
+ - legal
7
+ - ai4good
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+ license: apache-2.0
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+ language:
10
+ - en
11
+ metrics:
12
+ - accuracy
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+ base_model:
14
+ - google/gemma-3-4b-it
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+ pipeline_tag: text-generation
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+ ---
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  # GEMMA Document Rewriter for RAG Pipeline
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  ## Overview
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+ The **GEMMA Document Rewriter for RAG Pipeline** is a state-of-the-art text rewriting model built on top of the pre-trained [Google Gemma 3 4B](https://huggingface.co/unsloth/gemma-3-4b-it) language model. This model has been fine-tuned using a LoRA (Low-Rank Adaptation) technique, with the adapter weights provided by [ZySec-AI/gemma-3-4b-document-writer-lora](https://huggingface.co/ZySec-AI/gemma-3-4b-document-writer-lora). The primary goal of this model is to intelligently rewrite documents by eliminating unnecessary information, byte spaces, and redundant content. It extracts and emphasizes the information that is significant for Retrieval-Augmented Generation (RAG) pipelines, outputting a clean, structured version of the document in Markdown format with appropriate headings.
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  ## Key Features
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