Create README.md

README.md

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+---
+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.