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Model: arshaan-nazir/qwen2.5-3b-humanizer-merged Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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tags:
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- text-generation
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- humanizer
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- rewriter
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- qwen2.5
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- qlora
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- merged
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- conversational
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pipeline_tag: text-generation
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---
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# Qwen2.5-3B AI Text Humanizer (Merged)
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A fine-tuned version of **Qwen/Qwen2.5-3B-Instruct** that rewrites AI-generated text to sound natural and human-written. This is the **fully merged** model — the LoRA adapter weights have been baked directly into the base model, making it compatible with fast inference engines like **vLLM**.
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> ⚠️ This is a **private model**. You need a HuggingFace token with read access to load it.
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | `Qwen/Qwen2.5-3B-Instruct` |
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| Fine-tuning method | QLoRA (r=64, α=128) |
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| Training dataset | `qwertyuiopasdfg/English_humanize` (20k rows) |
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| Adapter repo | `arshaan-nazir/qwen2.5-3b-humanizer-qlora` |
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| Model type | Causal LM — merged weights |
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| Language | English |
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| License | Apache 2.0 |
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## What It Does
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Takes AI-generated text as input and rewrites it to:
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- Sound natural and conversational
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- Use contractions where appropriate
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- Vary sentence length and structure
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- Avoid stiff, formal phrasing
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- Preserve all original facts
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## How to Use
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### With Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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HF_TOKEN = "hf_YOUR_TOKEN_HERE" # needs read access to this repo
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print("Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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token=HF_TOKEN,
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)
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print("Loading model...")
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model = AutoModelForCausalLM.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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token=HF_TOKEN,
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)
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model.eval()
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SYSTEM = """You are a helpful editor.
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Rewrite the input so it sounds natural, clear, and conversational while preserving every fact.
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Rules:
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- Vary sentence length and structure (mix short and long).
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- Use contractions where natural.
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- Avoid stiff/overly formal phrases.
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- No bullet points, headings, or numbered lists.
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- Keep paragraphs reasonable (no more than 5 sentences per paragraph).
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- Output ONLY the rewritten text (no preamble, no labels).
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"""
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def humanize(text):
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msgs = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": f"Rewrite the following so it sounds fully human-written.\n\nTEXT:\n{text}\n\nREWRITE:"}
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]
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prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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with torch.no_grad():
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output = model.generate(
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input_ids,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.75,
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top_p=0.92,
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repetition_penalty=1.08,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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return tokenizer.decode(output[0, input_ids.shape[1]:], skip_special_tokens=True)
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text = "The utilization of artificial intelligence has resulted in significant advancements."
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print(humanize(text))
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```
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### With vLLM (recommended — fastest)
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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HF_TOKEN = "hf_YOUR_TOKEN_HERE"
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tokenizer = AutoTokenizer.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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token=HF_TOKEN,
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)
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llm = LLM(
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model="arshaan-nazir/qwen2.5-3b-humanizer-merged",
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dtype="bfloat16",
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gpu_memory_utilization=0.90,
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max_model_len=4096,
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enforce_eager=True,
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)
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sampling_params = SamplingParams(
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temperature=0.75,
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top_p=0.92,
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repetition_penalty=1.08,
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max_tokens=1024,
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)
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SYSTEM = """You are a helpful editor.
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Rewrite the input so it sounds natural, clear, and conversational while preserving every fact.
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Rules:
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- Vary sentence length and structure (mix short and long).
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- Use contractions where natural.
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- Avoid stiff/overly formal phrases.
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- No bullet points, headings, or numbered lists.
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- Keep paragraphs reasonable (no more than 5 sentences per paragraph).
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- Output ONLY the rewritten text (no preamble, no labels).
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"""
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text = "Your AI-generated text here..."
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msgs = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": f"Rewrite the following so it sounds fully human-written.\n\nTEXT:\n{text}\n\nREWRITE:"}
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]
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prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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outputs = llm.generate([prompt], sampling_params)
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print(outputs[0].outputs[0].text.strip())
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```
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## Recommended Inference Settings
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| Parameter | Value |
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|---|---|
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| `temperature` | 0.75 |
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| `top_p` | 0.92 |
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| `repetition_penalty` | 1.08 |
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| `max_new_tokens` | 512–1024 |
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## Training Details
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- **Base model:** `Qwen/Qwen2.5-3B-Instruct`
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- **Method:** QLoRA with 4-bit NF4 quantization during training
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- **LoRA rank:** 64, alpha: 128
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- **Target modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
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- **Dataset:** `qwertyuiopasdfg/English_humanize` — 20k (input, output) pairs of AI-generated vs human-written text
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- **Epochs:** 3
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- **Optimizer:** paged_adamw_8bit
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- **Learning rate:** 2e-4 with cosine schedule
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## Difference from Adapter Repo
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| | [`qwen2.5-3b-humanizer-qlora`](https://huggingface.co/arshaan-nazir/qwen2.5-3b-humanizer-qlora) | This repo |
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|---|---|---|
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| Type | LoRA adapter only | Fully merged model |
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| Requires base model | ✅ Yes | ❌ No |
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| vLLM compatible | ❌ No | ✅ Yes |
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| Size | ~120 MB | ~6 GB |
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| Load time | Slower (loads base + adapter) | Faster (single model) |
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## Live Demo
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Try it at: [arshaan-nazir/ai-text-humanizer](https://huggingface.co/spaces/arshaan-nazir/ai-text-humanizer-1771785713)
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## Developer
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**arshaan-nazir** — [HuggingFace Profile](https://huggingface.co/arshaan-nazir)
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