base_model:

• Unbabel/Tower-Plus-72B
  language:
• de
• nl
• is
• es
• fr
• pt
• uk
• hi
• zh
• ru
• cs
• ko
• ja
• it
• en
• da
• pl
• hu
• sv
• 'no'
• ro
• fi
  library_name: transformers
  license: cc-by-nc-sa-4.0
  pipeline_tag: text-generation
  tags:
• heretic
• uncensored
• decensored
• abliterated
• mpoa
• visual novels

95% fewer refusals (5/100 Uncensored vs 100/100 Original) while preserving model quality (0.0516 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.

This is a decensored version of Unbabel/Tower-Plus-72B, made using Heretic v1.4.0 with a variant of the Magnitude-Preserving Orthogonal Ablation (MPOA) method

Abliteration parameters

Targeted components

• attn.o_proj
• mlp.down_proj

Performance

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates more closeness to the original model's baseline. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections.

GGUF Version

GGUF quantizations available here llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF.

This repository contains the Tower+ 72B model, as presented in the paper Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs.

Project Page: https://huggingface.co/collections/Unbabel/tower-plus-6846ca452a10c0905dc03c0f

Model Description:

Tower+ 72B is build on top of Qwen 2.5 72B. The model goes through the Continuous Pretraining (CPT), Instruction Tuning (IT) and Weighted Preference Optimization (WPO). During all these stages we include parallel and multilingual data (covering 22 languages).

• Developed by: Unbabel
• Model type: A 72B parameter model fine-tuned on a mix of translation-related tasks as well as general instruction-following datasets that include reasoning, code instructions, etc.
• Languages: German, Spanish, French, Italian, Korean, Dutch, Russian, English, Portuguese (Portugal), Portuguese (Brazilian), Spanish (Latin America), Chinese (Simplified), Chinese (Traditional), Czech, Ukrainian, Hindi, Icelandic, Japanese, Polish, Swedish, Hungarian, Romanian, Danish, Norwegian (Nynorsk), Norwegian (Bokmål), Finnish
• License: CC-BY-NC-4.0
• Context Size:: 131,072 tokens (recommended generation tokens 8192)

Intended uses & limitations

Tower is intended for multilingual tasks and its specially strong on translation related tasks.

Another usecase Tower works well is for creating multilingual synthethic data (for the languages it covers). You can do this either by translating instructions and the respective answers or by asking the model to create an instruction given a document as seed data.

Usage:

When using the model, make sure your prompt is formated correctly!

Also, we recommend using VLLM rather than Hugging Face.

Using on VLLM:

# pip install vllm

from vllm import LLM, SamplingParams
sampling_params = SamplingParams(
  best_of=1,
  temperature=0,
  max_tokens=8192,
)
llm = LLM(model="Unbabel/Tower-Plus-72B", tensor_parallel_size=4)
messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
outputs = llm.chat(messages, sampling_params)
# Make sure your prompt_token_ids look like this
print (outputs[0].outputs[0].text)
# > Olá, mundo!

Using on Transformers:

# pip install transformers
# pip install accelerate
import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-72B", device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
outputs = pipe(messages, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])

Citation

If you use this model please cite our paper:

@misc{rei2025towerplus,
      title={Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs}, 
      author={Ricardo Rei and Nuno M. Guerreiro and José Pombal and João Alves and Pedro Teixeirinha and Amin Farajian and André F. T. Martins},
      year={2025},
      eprint={2506.17080},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.17080}, 
}