Instructions to use BidirLM/BidirLM-270M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BidirLM/BidirLM-270M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BidirLM/BidirLM-270M-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("BidirLM/BidirLM-270M-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BidirLM/BidirLM-270M-Base: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/BidirLM/BidirLM-270M-Base/resolve/main/tokenizer.json
- Command line
-
hf download hf://BidirLM/BidirLM-270M-Base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BidirLM/BidirLM-270M-Base/resolve/main/tokenizer.json
16 MB
- Xet hash:
- 3040e55e786c0a482b1560d98abbf333734acb0678f24497d52dd47a4a85e376
- Size of remote file:
- 16 MB
- SHA256:
- f50a608b4265c5cd72d929893d43a67730b2486237d4f2f81296df06e3045439
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