Bug fixes and binder design improvements

README.md

@@ -54,16 +54,16 @@ Codebase, model weights, and model variants for ESMC are available through [Hugg
 
 There are two primary ways of running the ESM models: through the [**Biohub Platform**](https://biohub.ai/) or locally with Hugging Face. The Biohub Platform enables users to easily run inference with ESM models with minimal setup. Users interested in customizing or fine-tuning ESM models can use the models from Hugging Face.
 
-### Running ESMC Locally
-<a name="running-esmc-locally"></a>
+### Running ESMC Through Hugging Face
+<a name="running-esmc-through-hugging-face"></a>
 
-Install `esm` from GitHub (a PyPI release is coming soon):
+First, install `esm` from GitHub (a PyPI release is coming soon):
 
 ```
 pip install esm@git+https://github.com/Biohub/esm.git@main
 ```
 
-The following code demonstrates how to run ESMC locally
+Then use the following code to run ESMC using the Transformers library via Hugging Face:
 
 ```python
 import torch
@@ -144,6 +144,16 @@ The sparse autoencoder used in the Atlas and analyzed in the paper, `ESMC-6B-sae
 
 Codebase, model weights, and model variants for ESMC SAEs are available through [Hugging Face](https://huggingface.co/collections/biohub/esmc-saes-for-hidden-states-all-layers).
 
+### Running SAEs Through Hugging Face
+
+First, install `esm` from GitHub (a PyPI release is coming soon):
+
+```
+pip install esm@git+https://github.com/Biohub/esm.git@main
+```
+
+Then use the following code to set up an ESMC SAE using the Transformers library via Hugging Face:
+
 ```python
 import torch
 from transformers import AutoModel, AutoTokenizer
@@ -170,19 +180,30 @@ output["sae_outputs"]["layer60"]  # sparse.coo tensor
 print(output["sae_outputs"]["layer60"].shape)
 
 ```
+### Running SAEs Through The Biohub Platform
 
-For tutorials on how to use ESMC SAEs, see our [tutorials](https://github.com/Biohub/esm/tree/main/cookbook/tutorials).
+For a tutorial on using SAEs using the Biohub Platform, see [here](https://github.com/Biohub/esm/blob/main/cookbook/tutorials/esmc_sae_feature_interpretation.ipynb).
 
 ## ESMFold2
 <a name="esmfold2"></a>
 
+
 [ESMFold2](https://huggingface.co/biohub/ESMFold2) is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model embeddings with a diffusion-based structure prediction architecture.
 
 The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for enhanced accuracy on challenging targets. ESMFold2 achieves state-of-the-art performance matching or exceeding AlphaFold3 across diverse evaluation datasets, while offering improved computational efficiency through optimized diffusion sampling and architectural innovations.
 
 Codebase, model weights, and model variants for ESMFold2 are available through [Hugging Face](https://huggingface.co/biohub/ESMFold2)
 
-### Running ESMFold2 Locally
+### Running ESMFold2 Through Hugging Face
+<a name="running-esmfold2-through-hugging-face"></a>
+
+First, install `esm` from GitHub (a PyPI release is coming soon):
+
+```
+pip install esm@git+https://github.com/Biohub/esm.git@main
+```
+
+Then use the following code to run ESMFold2 locally using the Transformers library via Hugging Face:
 
 ```python
 from esm.models.esmfold2 import (