Instructions to use NorGLM/NorGPT-3B-rfhl-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NorGLM/NorGPT-3B-rfhl-summarization with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="NorGLM/NorGPT-3B-rfhl-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NorGLM/NorGPT-3B-rfhl-summarization") model = AutoModelForCausalLM.from_pretrained("NorGLM/NorGPT-3B-rfhl-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NorGLM/NorGPT-3B-rfhl-summarization: direct link, hf CLI and curl.
- Browser
- Download file 5.91 GB
-
https://huggingface.co/NorGLM/NorGPT-3B-rfhl-summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NorGLM/NorGPT-3B-rfhl-summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NorGLM/NorGPT-3B-rfhl-summarization/resolve/main/pytorch_model.bin
5.91 GB
- Xet hash:
- f5a3392062b707f2647c4ca4e505fc94d9cce022aabf8f694663d73e2ae330d1
- Size of remote file:
- 5.91 GB
- SHA256:
- ce2eb3c2bf79d2e987077993f8dba5882379cc1843fad64f8309c1bacda04d6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.