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:
# pip install -U transformers accelerate # 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
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Download UPSTREAM_NOTICE.md from zpointsun/DiBO-TFBind8: direct link, hf CLI and curl.
- Browser
- Download file 376 Bytes
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https://huggingface.co/zpointsun/DiBO-TFBind8/resolve/main/UPSTREAM_NOTICE.md
- Command line
-
hf download hf://zpointsun/DiBO-TFBind8/UPSTREAM_NOTICE.md
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curl -L -o UPSTREAM_NOTICE.md https://huggingface.co/zpointsun/DiBO-TFBind8/resolve/main/UPSTREAM_NOTICE.md
376 Bytes
Upstream LLaDA code notice
configuration_llada.py and modeling_llada.py are copied from GSAI-ML/LLaDA-8B-Instruct revision 08b83a6feb34df1a6011b80c3c00c7563e963b07. The base model card declares the MIT license. The copied files remain subject to the upstream terms. modeling_dibo_llada.py is DiBO release code.