Instructions to use Intel/dynamic_tinybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dynamic_tinybert with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" 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("question-answering", model="Intel/dynamic_tinybert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert") model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Intel/dynamic_tinybert: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Intel/dynamic_tinybert/resolve/19d76432143e4fa5f11ac5241743299c14ac805d/model.safetensors
- Command line
-
hf download hf://Intel/dynamic_tinybert@19d76432143e4fa5f11ac5241743299c14ac805d/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Intel/dynamic_tinybert/resolve/19d76432143e4fa5f11ac5241743299c14ac805d/model.safetensors
268 MB
- Xet hash:
- 51b97d94515679bc0269c3dd9d88bd66b3bdc468cde1317b7c95a6b001a4eb6f
- Size of remote file:
- 268 MB
- SHA256:
- 3f4893e85630e59d3944c2b55bb980aeafa7858c2f2ade387e401e96dfe6341a
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.