Translation
Transformers
PyTorch
Safetensors
llama
text-generation
Eval Results (legacy)
text-generation-inference
Instructions to use Unbabel/TowerBase-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unbabel/TowerBase-7B-v0.1 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/TowerBase-7B-v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Unbabel/TowerBase-7B-v0.1") model = AutoModelForCausalLM.from_pretrained("Unbabel/TowerBase-7B-v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54f006acca2d17d94ca5b193e162dbc88ff2bd2948a63346443f8a805f1b4648
- Size of remote file:
- 13.5 GB
- SHA256:
- b891eb7c23cb21314b9ff8c0775a8cf42274b82bc3e4f9ee636f1a01815d2709
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.