Instructions to use JunxiongWang/BiGS_128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/BiGS_128 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JunxiongWang/BiGS_128")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("JunxiongWang/BiGS_128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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BiGS 1000k
Bidirectional Gated State Space Model. 346M parameters, 23 layers, max sentence length 128, train 1000k steps with 97,280 tokens per step0191d1e