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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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- de |
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- fr |
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- zh |
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- pt |
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- nl |
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- ru |
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- ko |
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- it |
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- es |
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metrics: |
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- comet |
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pipeline_tag: translation |
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--- |
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# Model Card for TowerBase-7B-v0.1 |
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## Model Details |
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### Model Description |
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TowerBase-7B 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-7B-v0.1 is the first model in the series. |
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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-7B-v0.1). |
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We will release more details in the upcoming technical report. |
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- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay |
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- **Model type:** A 7B parameter model built on top of Llama 2 by continuing pretraining on multilingual data. |
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- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian |
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- **License:** CC-BY-NC-4.0 |
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## Intended uses & limitations |
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The model is intended for research purposes in the 10 languages it supports. |
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The model is able to perform well on translation and related tasks (e.g., APE, GEC) on a few-shot regime. |
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It can also be fine-tuned to perform these tasks in a zero-shot fashion (see [TowerInstruct](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1), as well as other multilingual tasks. |
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### Out-of-Scope Use |
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The model is not guaranteed to perform well for languages other than the 10 languages it supports. |
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## Bias, Risks, and Limitations |
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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). |
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## Run the model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "Unbabel/TowerBase-7B-v0.1" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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text = "English: My name is TowerBase.\nPortuguese:" |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=20) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### Training Data |
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Filtered versions of [mc4](https://huggingface.co/datasets/mc4) and bilingual data from various sources (e.g., [OPUS](https://opus.nlpl.eu/)). |
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## Citation |
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To be completed. |
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