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Youmei295
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Model Details
Model Provider
Youmei295
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
DeAIze is a fine-tuned LoRA adapter based on the Qwen/Qwen2.5-3B-Instruct model. Its primary purpose is to "de-AI-ze" text—taking standard, often generic or robotic-sounding AI-generated text and rewriting it to match a more natural, personal writing style.
This model is intended to humanize AI-generated content. By providing draft text generated by other AI assistants, you can use this model to rephrase the content so that it flows naturally and mimics your specific personal tone and vocabulary.
Because this is a Parameter-Efficient Fine-Tuning (PEFT) model, you will need to load the base model and then apply this LoRA adapter on top of it. You can do this easily using the transformers and peft libraries.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_name = "Qwen/Qwen2.5-3B-Instruct"adapter_model_name = "Youmei295/deAize" # Load the base tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(base_model_name)base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto") # Load the LoRA adaptermodel = PeftModel.from_pretrained(base_model, adapter_model_name) # Generate textprompt = "Rewrite this text to match my natural writing style: The utilization of advanced methodologies can significantly enhance operational efficiency."messages = [ {"role": "system", "content": "You are a helpful assistant that rewrites text into a natural, personal writing style."}, {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))This model was trained using Low-Rank Adaptation (LoRA) to efficiently update the base model's weights.
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_name = "Qwen/Qwen2.5-3B-Instruct"adapter_model_name = "Youmei295/deAize" # Load the base tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(base_model_name)base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto") # Load the LoRA adaptermodel = PeftModel.from_pretrained(base_model, adapter_model_name) # Generate textprompt = "Rewrite this text to match my natural writing style: The utilization of advanced methodologies can significantly enhance operational efficiency."messages = [ {"role": "system", "content": "You are a helpful assistant that rewrites text into a natural, personal writing style."}, {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))