Instructions to use SanayCo/model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SanayCo/model_output 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="SanayCo/model_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SanayCo/model_output") model = AutoModelForQuestionAnswering.from_pretrained("SanayCo/model_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SanayCo/model_output: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/SanayCo/model_output/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://SanayCo/model_output/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SanayCo/model_output/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- f5d26a2bf2ce52a2e819826796ac97e978f5aac9a3032314ec7d6ed67df64935
- Size of remote file:
- 436 MB
- SHA256:
- 5dab4d2fd79b5a6126b0a2a0b640b1a967e0ba256e8154caf43a122cb7c56bc4
路
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