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Unbabel/Tower-Plus-2B
Tower-Plus-2B is a text generation model from Unbabel. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.
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From the Hugging Face model README

Tower+ 2B is build on top of Gemma 2 2B. The model goes through the Continuous Pretraining (CPT), Instruction Tuning (IT), Weighted Preference Optimization (WPO) and GRPO with verifiable rewards. During all stages we include parallel and multilingual data (covering 22 languages).
This approach makes Tower+ 2B one of the best multilingual LLMs under 3B parameters.
Tower is intended for multilingual tasks and its specially strong on machine translation.
Because Tower is also a strong multilingual model you can also use it for other multilingual 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.
When using the model, make sure your prompt is formated correctly!
Also, we recommend using VLLM rather than Hugging Face.
# pip install vllm
# Gemma by default only uses 4k context. You need to set the following variables:
# export VLLM_WORKER_MULTIPROC_METHOD=spawn
# export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
from vllm import LLM, SamplingParams
sampling_params = SamplingParams(
best_of=1,
temperature=0,
max_tokens=8192,
)
llm = LLM(model="Unbabel/Tower-Plus-2B", tensor_parallel_size=1)
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!
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-2B", 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"])
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},
}