| --- |
| 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.* |
|
|
