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license: openmdw-1.1
base_model:
- unsloth/Llama-3.2-3B-bnb-4bit
tags:
- aksern
- humanizer
- text-rewriting
- llama
- 3b
- text-generation
pipeline_tag: text-generation
---
# aksern humanizer
**a lightweight text humanization model built on llama 3.2 3b.**
aksern humanizer is a fine-tuned language model designed to rewrite AI-generated text into more natural, human-like prose while preserving the original meaning.
built on top of `meta-llama/Llama-3.2-3B`, the model focuses on text transformation rather than general-purpose conversation. its goal is to make generated text sound less formulaic, repetitive, and machine-written without unnecessarily changing the underlying information.
## overview
| property | value |
|---|---|
| developer | aksern |
| model type | causal language model |
| base model | meta-llama/Llama-3.2-3B |
| parameter count | 3B |
| primary task | text humanization |
| architecture | Llama 3.2 |
| intended use | rewriting AI-generated text |
| inference | local or hosted, depending on deployment |
## features
- **natural rewriting** - transforms formulaic AI-generated prose into more natural language.
- **meaning preservation** - aims to retain the original information and intent.
- **lightweight inference** - a 3B-parameter model suitable for local deployment on compatible hardware.
- **focused behavior** - optimized for text transformation instead of unrestricted general-purpose generation.
## usage
provide a piece of text and let the model rewrite it while preserving its meaning. example code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
path = "aksern/humanizer"
text = "In today's rapidly evolving technological landscape, it is important to note that artificial intelligence plays a significant role in various aspects of our daily lives."
model = AutoModelForCausalLM.from_pretrained(path, dtype=torch.bfloat16, device_map="auto")
tok = AutoTokenizer.from_pretrained(path)
inputs = tok.apply_chat_template(
[{"role": "user", "content": text}],
add_generation_prompt=False, tokenize=True, return_dict=True, return_tensors="pt",
).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs, max_new_tokens=120, do_sample=True,
temperature=0.6, top_p=0.9, pad_token_id=tok.eos_token_id,
)
out_ids = outputs[0][inputs["input_ids"].shape[-1]:]
if tok.eos_token_id not in out_ids.tolist():
print("WARNING: generation hit max_new_tokens, not EOS")
print("--- GENERATED ---")
print(repr(tok.decode(out_ids, skip_special_tokens=True)))
```
### example
**input**
> In today's rapidly evolving technological landscape, it is important to note that artificial intelligence plays a significant role in various aspects of our daily lives.
**humanized output**
> In this fast paced technological world, we need to realize that artificial intelligence is a major factor in many aspects of our lives.
## intended use
- rewriting AI-generated drafts into more natural prose
- reducing repetitive or overly formulaic phrasing
- improving the readability and flow of generated text
- experimenting with lightweight text-humanization models
## limitations
- Humanization quality depends on the input text, language, and generation settings.
- The model may not perform equally well across languages, domains, and writing styles.
- More natural-sounding text does not necessarily mean more accurate text.
- Humanization and AI-text detection are different tasks. The model is not guaranteed to bypass AI detectors.
## acknowledgements
Built by **aksern**.
*models that run where you are.*
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