Instructions to use hfl/rbt4-h312 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt4-h312 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt4-h312")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt4-h312") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt4-h312", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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In order to further promote the research and development of Chinese information processing, we launched a Chinese small pre-training model MiniRBT based on the self-developed knowledge distillation tool TextBrewer, combined with Whole Word Masking technology and Knowledge Distillation technology.
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This repository is developed based on:https://github.com/iflytek/
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You may also interested in,
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- Chinese LERT: https://github.com/ymcui/LERT
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In order to further promote the research and development of Chinese information processing, we launched a Chinese small pre-training model MiniRBT based on the self-developed knowledge distillation tool TextBrewer, combined with Whole Word Masking technology and Knowledge Distillation technology.
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This repository is developed based on:https://github.com/iflytek/MiniRBT
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You may also interested in,
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- Chinese LERT: https://github.com/ymcui/LERT
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