Instructions to use Unbabel/TowerInstruct-Mistral-7B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unbabel/TowerInstruct-Mistral-7B-v0.2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="Unbabel/TowerInstruct-Mistral-7B-v0.2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Unbabel/TowerInstruct-Mistral-7B-v0.2") model = AutoModelForCausalLM.from_pretrained("Unbabel/TowerInstruct-Mistral-7B-v0.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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### Model Description
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The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and paragraph/document-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
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This model has performance comparable to the larger TowerInstruct-13B-v0.2, while being roughly half the size. Check out our [paper](https://arxiv.org/abs/2402.17733).
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### Model Description
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TowerInstruct-Mistral-7B-v0.2 is a language model that results from fine-tuning a Mistral version of TowerBase on the TowerBlocks supervised fine-tuning dataset.
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The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and paragraph/document-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
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This model has performance comparable to the larger TowerInstruct-13B-v0.2, while being roughly half the size. Check out our [paper](https://arxiv.org/abs/2402.17733).
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