Reinforcement Learning
Transformers
Safetensors
llada
feature-extraction
llm
diffusion-language-model
black-box-optimization
offline-black-box-optimization
design-bench
dibo
custom_code
Instructions to use zpointsun/DiBO-TFBind8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zpointsun/DiBO-TFBind8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zpointsun/DiBO-TFBind8", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fc0f5ad3b36f8cd2d8eedc5d5735fed26f09d62c7036fbaaf0189bf03936f46
- Size of remote file:
- 16 GB
- SHA256:
- 0d8e17f7aa60baf4173d71a4ec8475d182a992812b59d29a728eec719d1b67bf
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