Instructions to use BSC-LT/salamandraTA-2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/salamandraTA-2b-instruct 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="BSC-LT/salamandraTA-2b-instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/salamandraTA-2b-instruct") model = AutoModelForCausalLM.from_pretrained("BSC-LT/salamandraTA-2b-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download images/salamandra_header.png from BSC-LT/salamandraTA-2b-instruct: direct link, hf CLI and curl.
- Browser
- Download file 11.1 MB
-
https://huggingface.co/BSC-LT/salamandraTA-2b-instruct/resolve/main/images/salamandra_header.png
- Command line
-
hf download hf://BSC-LT/salamandraTA-2b-instruct/images/salamandra_header.png
-
curl -L -o salamandra_header.png https://huggingface.co/BSC-LT/salamandraTA-2b-instruct/resolve/main/images/salamandra_header.png
11.1 MB
- Xet hash:
- 75794a113c832df9ec5c307ded2e044b0e5fc594c26d87352adc152b864d1f30
- Size of remote file:
- 11.1 MB
- SHA256:
- de12bec43f22c0c41b45b84425759d6c9e38ecdf06d58519f048f10fe6e826de
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