Instructions to use am-infoweb/rap_phase2_11jan_15i_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use am-infoweb/rap_phase2_11jan_15i_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="am-infoweb/rap_phase2_11jan_15i_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("am-infoweb/rap_phase2_11jan_15i_v2") model = AutoModelForQuestionAnswering.from_pretrained("am-infoweb/rap_phase2_11jan_15i_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from am-infoweb/rap_phase2_11jan_15i_v2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/am-infoweb/rap_phase2_11jan_15i_v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://am-infoweb/rap_phase2_11jan_15i_v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/am-infoweb/rap_phase2_11jan_15i_v2/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- d9f5c2ec200d52fe11c36b90888a68140ad97b5a64ea8b4f1c6bea36dc1c2a8d
- Size of remote file:
- 1.11 GB
- SHA256:
- 1041511cf8c0898279af7a0f4459f3ceee315c52ef151068cd8222386642b45e
路
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