---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
pipeline_tag: text-generation
license: mit
language:
- en
tags:
- base_model:adapter:Qwen/Qwen2.5-3B-Instruct
- lora
- transformers
- academic-writing
- paraphrasing
---

# Automated Humanizer — LoRA adapter

LoRA adapter for `Qwen/Qwen2.5-3B-Instruct` that rewrites LLM-drafted academic
text to read as human-authored, while preserving the scientific content. It is
the default `local_lora` backend of the
[automated_humanizer](https://github.com/avasil02/automated_humanizer)
pipeline, which pairs each rewrite with an AI-detector gate and an LLM
academic-validity judge.

- **Base model:** Qwen/Qwen2.5-3B-Instruct (load the base separately; this repo
  contains only the adapter)
- **Training data:** synthetic (AI-draft, human-original) paragraph pairs
  built from award-winning pre-2020 arXiv papers (see the project's
  `training/` subsystem)
- **Hardware:** trained and runs fully offline on an NVIDIA Jetson Orin
  (~6 GB GPU/unified RAM at inference)

## Usage

Intended to be used through the pipeline — see the
[project README](https://github.com/avasil02/automated_humanizer). Standalone:

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base, "avasil02/automated-humanizer-lora")
tokenizer = AutoTokenizer.from_pretrained("avasil02/automated-humanizer-lora")
```

## Intended use & limitations

Edits *style* only; the surrounding pipeline verifies that the original
results and claims are preserved. It is for making genuine research read
naturally — not for misrepresenting authorship of the underlying work.
Trained on English academic prose; quality outside that domain is untested.

### Framework versions

- PEFT 0.19.1