Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
ONNX
t5
text2text-generation
summarization
text-generation-inference
Instructions to use altfreq/t5-small-temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use altfreq/t5-small-temp 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="altfreq/t5-small-temp")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("altfreq/t5-small-temp") model = AutoModelForSeq2SeqLM.from_pretrained("altfreq/t5-small-temp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b70d99309aade2be2757183953aff1be3a6090b09225ec2a1224d8d25ae16d5
- Size of remote file:
- 242 MB
- SHA256:
- b143e13ccb7307ad36b3327ca49019f222606e945d9b995404d7200224504a9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.