---
license: cc-by-nc-4.0
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
metrics:
- comet
pipeline_tag: translation
---
# Model Card for TowerBase-13B-v0.1

## Model Details

### Model Description

TowerBase-13B is a language model that results from continuing the pretraining of Llama 2 on a mix of 20 billion tokens of monolingual data in ten different languages — English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian — and bilingual data. TowerBase-13B-v0.1 is the first model in the series. 
The resulting model shows improved performance on the supported languages, while maintaining Llama 2's capabilities on English. It is particularly well-suited for fine-tuning on translation and related tasks: check out [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-13B-v0.1).

We will release more details in the upcoming technical report.

- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay 
- **Model type:** A 13B parameter model built on top of Llama 2 by continuing pretraining on multilingual data.
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
- **License:** CC-BY-NC-4.0, Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

## Intended uses & limitations

The model is intended for research purposes in the 10 languages it supports.
The model is able to perform well on translation and related tasks (e.g., APE, GEC) on a few-shot regime. 
It can also be fine-tuned to perform these tasks in a zero-shot fashion (see [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-13B-v0.1), as well as other multilingual tasks.

### Out-of-Scope Use

The model is not guaranteed to perform well for languages other than the 10 languages it supports.

## Bias, Risks, and Limitations

TowerBase-v0.1 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements). 

## Run the model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Unbabel/TowerBase-13B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "English: My name is TowerBase.\nPortuguese:"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Training Data

Filtered versions of [mc4](https://huggingface.co/datasets/mc4) and bilingual data from various sources (e.g., [OPUS](https://opus.nlpl.eu/)).

## Citation 

To be completed.