Instructions to use huawei-noah/JABERv2-6L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huawei-noah/JABERv2-6L with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="huawei-noah/JABERv2-6L")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("huawei-noah/JABERv2-6L") model = AutoModelForMaskedLM.from_pretrained("huawei-noah/JABERv2-6L", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from huawei-noah/JABERv2-6L: direct link, hf CLI and curl.
- Browser
- Download file 371 MB
-
https://huggingface.co/huawei-noah/JABERv2-6L/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://huawei-noah/JABERv2-6L/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/huawei-noah/JABERv2-6L/resolve/main/pytorch_model.bin
371 MB
- Xet hash:
- 28ad7fd41d4cea691ea17af160541930366a62384a4a739d973ba7a7e1796106
- Size of remote file:
- 371 MB
- SHA256:
- dfac5a21370bfdbe633758b81ed48ecd29b389046839aeacfc2528141eadd0da
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