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@@ -28,7 +28,7 @@ The ESMC 6B model has 6 billion parameters, with 80 layers and 2.37e23 training
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  The [ESMFold2](https://huggingface.co/biohub/ESMFold2) structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy.
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- The [ESMC sparse autoencoder](https://huggingface.co/Biohub/ESMC-6B-sae-layer60-k64-codebook16384), `ESMC-6B-sae-layer60-k64-codebook16384`, is built on the ESMC 6B model and provides human-interpretable, agent-generated feature descriptions. See the [ESMC SAE overview card](https://huggingface.co/Biohub/ESMC-SAE-Overview) for the full set of ESMC SAE variants.
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  To run this model with the Biohub Platform API, visit the [Biohub Platform](https://biohub.ai/).
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@@ -39,7 +39,7 @@ Read more about ESMC in our paper [here](https://biohub.ai/papers/esm_protein.pd
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  Install `esm` from GitHub (a PyPI release is coming soon):
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  ```
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- pip install esm@git+https://github.com/Biohub/esm.git@c94ed8d
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  ```
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  ```py
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  | Model Variant | Description | URL |
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  | :---- | :---- | :---- |
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- | ESMC 300M | Smallest variant, publicly released. | [https://huggingface.co/Biohub/ESMC-300M](https://huggingface.co/Biohub/ESMC-300M) |
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- | ESMC 600M | Medium variant, publicly released. | [https://huggingface.co/Biohub/ESMC-600M](https://huggingface.co/Biohub/ESMC-600M) |
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- | ESMC 6B | Large variant, publicly released. | [https://huggingface.co/Biohub/ESMC-6B](https://huggingface.co/Biohub/ESMC-6B) |
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  ### System Requirements
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  The [ESMFold2](https://huggingface.co/biohub/ESMFold2) structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy.
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+ The [ESMC sparse autoencoder](https://huggingface.co/biohub/ESMC-6B-sae-layer60-k64-codebook16384), `ESMC-6B-sae-layer60-k64-codebook16384`, is built on the ESMC 6B model and provides human-interpretable, agent-generated feature descriptions. See the [ESMC SAE overview card](https://huggingface.co/biohub/ESMC-SAE-Overview) for the full set of ESMC SAE variants.
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  To run this model with the Biohub Platform API, visit the [Biohub Platform](https://biohub.ai/).
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  Install `esm` from GitHub (a PyPI release is coming soon):
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  ```
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+ pip install esm@git+https://github.com/Biohub/esm.git@main
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  ```
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  ```py
 
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  | Model Variant | Description | URL |
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  | :---- | :---- | :---- |
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+ | ESMC 300M | Smallest variant, publicly released. | [https://huggingface.co/biohub/ESMC-300M](https://huggingface.co/biohub/ESMC-300M) |
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+ | ESMC 600M | Medium variant, publicly released. | [https://huggingface.co/biohub/ESMC-600M](https://huggingface.co/biohub/ESMC-600M) |
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+ | ESMC 6B | Large variant, publicly released. | [https://huggingface.co/biohub/ESMC-6B](https://huggingface.co/biohub/ESMC-6B) |
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  ### System Requirements
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