Update README.md

README.md

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-# Model Card for ESMCFold-Fast
+# Model Card for ESMFold2-Fast
 
 ## Model Details
 
-ESMCFold is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model representations with a diffusion-based structure prediction architecture inspired by AlphaFold3. 
+ESMFold2 is a state-of-the-art protein structure prediction model that combines ESMC (6B parameter) language model representations with a diffusion-based structure prediction architecture inspired by AlphaFold3. 
 
-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. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMCFold is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids.
+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. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMFold2 is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids.
 
-ESMCFold-Fast is an Inference optimized single-sequence structure prediction model and is not MSA conditioned.
+ESMFold2-Fast is an Inference optimized single-sequence structure prediction model and is not MSA conditioned.
 
 For additional information, visit the [Biohub Platform](https://biohub.ai) for no-code tools, step-by-step tutorial notebooks, and detailed information on the models.
 
@@ -54,7 +54,7 @@ This hybrid architecture combines the representational power of large-scale prot
    
 - This efficient sampling strategy significantly reduces computational cost while maintaining accuracy
 
-**4\. Optional MSA Encoder (ESMCFold MSA variant):**
+**4\. Optional MSA Encoder (ESMFold2 MSA variant):**
 
 - Processes multiple sequence alignments to extract evolutionary information  
    
@@ -76,8 +76,8 @@ This hybrid architecture combines the representational power of large-scale prot
 
 | Model | MSA Conditioning | Description | Data Cutoff |
 | :---- | :---- | :---- | :---- |
-| [esmcfold-fast-2605](https://huggingface.co/biohub/esmcfold-fast) | No | Inference optimized single-sequence structure prediction model | June 2025, older cutoff is Sept 2021 |
-| [esmcfold-2605](https://huggingface.co/biohub/esmcfold) | Yes | Large model, capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets | June 2025, older cutoff is Sept 2021 |
+| [ESMFold2-fast](https://huggingface.co/biohub/esmfold2-fast) | No | Inference optimized single-sequence structure prediction model | June 2025, older cutoff is Sept 2021 |
+| [ESMFold2](https://huggingface.co/biohub/esmfold2) | Yes | Large model, capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets | June 2025, older cutoff is Sept 2021 |
 
 **Confidence Head (optional, for inference):**
 
@@ -88,7 +88,7 @@ This hybrid architecture combines the representational power of large-scale prot
 
 ### Primary Use Cases
 
-ESMCFold-Fast is designed to handle a wide range of structural prediction tasks, including:
+ESMFold2-Fast is designed to handle a wide range of structural prediction tasks, including:
 
 #### **Complex biomolecular interactions**
 
@@ -121,7 +121,7 @@ The model was trained on sequences from [PDB](https://www.rcsb.org/) and [AlphaF
 
 ## Performance Metrics
 
-ESMCfold was evaluated against state-of-the-art single-sequence and MSA-based structure prediction models on the FoldBench benchmark. ESMCFold meets or exceeds performance by AlphaFold3 on antibody-antigen complex prediction, protein-protein complex prediction and [Runs N’ Poses](https://www.biorxiv.org/content/10.1101/2025.02.03.636309v1) benchmarks. For inference time-scaling on FoldBench, ESMCFold scales gracefully with sample count, comparable to or exceeding AlphaFold3’s scaling behavior on the same targets.
+ESMFold2 was evaluated against state-of-the-art single-sequence and MSA-based structure prediction models on the FoldBench benchmark. ESMFold2 meets or exceeds performance by AlphaFold3 on antibody-antigen complex prediction, protein-protein complex prediction and [Runs N’ Poses](https://www.biorxiv.org/content/10.1101/2025.02.03.636309v1) benchmarks. For inference time-scaling on FoldBench, ESMFold2 scales gracefully with sample count, comparable to or exceeding AlphaFold3’s scaling behavior on the same targets.
 
 Refer to the [paper](https://biohub.ai/papers/esmc.pdf) for details on additional performance metrics.
 
@@ -176,3 +176,4 @@ Should you have any security or privacy issues or questions related to this mode
 
 Many people on the Biohub AI Research team and prior EvolutionaryScale team contributed to the development of this model. It would not have been possible without them.
 
+