Instructions to use Jingya/tiny-random-bert-remote-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingya/tiny-random-bert-remote-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jingya/tiny-random-bert-remote-code")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jingya/tiny-random-bert-remote-code") model = AutoModel.from_pretrained("Jingya/tiny-random-bert-remote-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download create_model.py from Jingya/tiny-random-bert-remote-code: direct link, hf CLI and curl.
- Browser
- Download file 387 Bytes
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https://huggingface.co/Jingya/tiny-random-bert-remote-code/resolve/db5d8988ddeb0d4a4a80f8e5133a3f5d9e5ea18f/create_model.py
- Command line
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hf download hf://Jingya/tiny-random-bert-remote-code@db5d8988ddeb0d4a4a80f8e5133a3f5d9e5ea18f/create_model.py
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curl -L -o create_model.py https://huggingface.co/Jingya/tiny-random-bert-remote-code/resolve/db5d8988ddeb0d4a4a80f8e5133a3f5d9e5ea18f/create_model.py
387 Bytes
| # from transformers import AutoConfig | |
| # from modeling.modeling_bert import BertCustomLMHeadModel | |
| # cfg = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-BertModel") | |
| # BertCustomLMHeadModel.register_for_auto_class("AutoModelForSequenceClassification") | |
| # model = BertCustomLMHeadModel(cfg) | |
| # model.save_pretrained("/home/Jingya/hf_internship/tiny-testing-gpt2-remote-code") |