llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF · Hugging Face

Source: https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF

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https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#95-fewer-refusals-5100-uncensored-vs-100100-original-while-preserving-model-quality-00516-kl-divergence95% fewer refusals(5/100 Uncensored vs 100/100 Original) while preserving model quality (0.0516 KL divergence).

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#%E2%9D%A4%EF%B8%8F-support-my-work❤️ 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.

GGUF quantization ofllmfan46/Tower-Plus-72B-ultra-uncensored-heretic.

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#this-is-a-decensored-version-of-unbabeltower-plus-72b-made-using-heretic-v140-with-a-variant-of-the-magnitude-preserving-orthogonal-ablation-mpoa-methodThis is a decensored version ofUnbabel/Tower-Plus-72B, made usingHereticv1.4.0 with a variant of theMagnitude-Preserving Orthogonal Ablation (MPOA)method

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#abliteration-parametersAbliteration parameters

ParameterValuedirection_indexper layerattn.o_proj.max_weight1.27attn.o_proj.max_weight_position49.84attn.o_proj.min_weight1.12attn.o_proj.min_weight_distance30.99mlp.down_proj.max_weight1.49mlp.down_proj.max_weight_position52.92mlp.down_proj.min_weight1.36mlp.down_proj.min_weight_distance12.90

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#targeted-componentsTargeted components

• attn.o_proj
• mlp.down_proj

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#performancePerformance

MetricThis modelOriginal model (Tower-Plus-72B)KL divergence0.05160*(by definition)*Refusals✅5/100❌100/100 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.

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

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

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#model-descriptionModel Description:

Tower+ 72Bis 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 oftranslation-related tasksas well asgeneral instruction-followingdatasets 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)

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#intended-uses–limitationsIntended 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.

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#usageUsage:

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

Also, we recommend using VLLM rather than Hugging Face.

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#using-on-vllmUsing 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!

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#using-on-transformersUsing 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"])

https://huggingface.co/llmfan46/Tower-Plus-72B-ultra-uncensored-heretic-GGUF#citationCitation

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}, 
}