arshaan-nazir/qwen2.5-3b-humanizer-merged
The arshaan-nazir/qwen2.5-3b-humanizer-merged is a 3.1 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-3B-Instruct. Developed by arshaan-nazir, this model specializes in rewriting AI-generated text to sound natural and human-written. It achieves this by varying sentence structure, using contractions, and avoiding overly formal phrasing, making it ideal for enhancing text readability and authenticity.
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Overview
This model, arshaan-nazir/qwen2.5-3b-humanizer-merged, is a fine-tuned version of the Qwen/Qwen2.5-3B-Instruct base model, specifically designed to transform AI-generated text into more natural, human-sounding prose. It integrates the LoRA adapter weights directly into the base model, making it a fully merged model that is compatible with fast inference engines like vLLM.
Key Capabilities
- Humanizes AI Text: Rewrites AI-generated content to be conversational and natural.
- Preserves Facts: Ensures all original factual information remains intact during the rewriting process.
- Stylistic Adjustments: Varies sentence length and structure, incorporates contractions, and eliminates stiff or overly formal phrasing.
- Optimized for Inference: As a merged model, it offers faster load times and is compatible with vLLM for efficient deployment.
Training Details
The model was fine-tuned using QLoRA (r=64, α=128) on the qwertyuiopasdfg/English_humanize dataset, which consists of 20,000 pairs of AI-generated versus human-written English text. Training involved 3 epochs with a paged_adamw_8bit optimizer and a learning rate of 2e-4.
When to Use This Model
This model is particularly well-suited for applications requiring the conversion of robotic or overly formal AI output into engaging, human-like content. It's ideal for content creation, customer service responses, or any scenario where AI-generated text needs a natural, conversational tone without altering its core message.