biohub/esm3-sm-open-v1
https://huggingface.co/biohub/esm3-sm-open-v1esm3-sm-open-v1 is trained on 2.78 billion natural proteins. With synthetic data augmentation, this led to 3.15 billion protein sequences, 236 million protein structures, and 539 million proteins with function annotations, totaling 771 billion tokens.
Sourced from
- HuggingFace — biohub/esm3-sm-open-v1
Related resources
biohub/esmc-300m-2024-12
by biohubThis set of model weights was released with the GitHub-compatible esm package format. The models here are kept for backwards compatibility, but we recommend you use the HuggingFace-compatible model weights at biohub/ESMC-6B (or biohub/ESMC-300M / biohub/ESMC-600M) instead.
biohub/esmc-600m-2024-12
by biohubThis set of model weights was released with the GitHub-compatible esm package format. The models here are kept for backwards compatibility, but we recommend you use the HuggingFace-compatible model weights at biohub/ESMC-6B (or biohub/ESMC-300M / biohub/ESMC-600M) instead.
biohub/ESMC-600M
by biohubESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein…
mradermacher/Phi-4-Instruct-Bioaligned-GGUF
by mradermacherFor a convenient overview and download list, visit our model page for this model.
This model card provides an overview of the intended use of the ESMC SAE models and examples of how to access them, but it does not have a specific model or model weights. To access each SAE model collection, use the links below:
PurvaTijare/PPTStab
by PurvaTijarePPTStab: Prediction and Designing of thermostable proteins with a desired melting temperature