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--- |
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base_model: google/gemma-2-9b |
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license: cc-by-nc-sa-4.0 |
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language: |
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- de |
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- nl |
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- is |
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- es |
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- fr |
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- pt |
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- uk |
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- hi |
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- zh |
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- ru |
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- cs |
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- ko |
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- ja |
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- it |
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- en |
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- da |
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- pl |
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- hu |
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- sv |
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- 'no' |
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- ro |
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- fi |
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library_name: transformers |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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A fairly effective attempt at uncensoring Tower Plus, while maintaining some core functionality. |
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Below is taken directly from Unbabel/Tower-Plus-9B |
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# Usage: |
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When using the model, make sure your prompt is formated correctly! |
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Also, we recommend using VLLM rather than Hugging Face. |
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### Using on VLLM: |
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```python |
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# pip install vllm |
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# Gemma by default only uses 4k context. You need to set the following variables: |
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# export VLLM_WORKER_MULTIPROC_METHOD=spawn |
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# export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 |
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from vllm import LLM, SamplingParams |
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sampling_params = SamplingParams( |
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best_of=1, |
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temperature=0, |
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max_tokens=8192, |
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) |
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llm = LLM(model="Unbabel/Tower-Plus-9B", tensor_parallel_size=1) |
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messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}] |
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outputs = llm.chat(messages, sampling_params) |
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# Make sure your prompt_token_ids look like this |
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print (outputs[0].outputs[0].text) |
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# > Olá, mundo! |
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``` |
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### Using on Transformers: |
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```python |
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# Install transformers from source - only needed for versions <= v4.34 |
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# pip install git+https://github.com/huggingface/transformers.git |
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# pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-9B", device_map="auto") |
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# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}] |
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input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True) |
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outputs = pipe(messages, max_new_tokens=256, do_sample=False) |
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print(outputs[0]["generated_text"]) |
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``` |
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# Citation |
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``` |
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@misc{rei2025towerplus, |
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title={Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs}, |
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author={Ricardo Rei and Nuno M. Guerreiro and José Pombal and João Alves and Pedro Teixeirinha and Amin Farajian and André F. T. Martins}, |
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year={2025}, |
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eprint={2506.17080}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2506.17080}, |
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} |
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``` |
