Add DecoderTCR V0.3 weights (nested layout) + MIT model card

DecoderTCR-ESM2-V0.1/3B_DecoderTCR.ckpt

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DecoderTCR-ESMC-V0.3/6B.ckpt

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LICENSE

@@ -0,0 +1,9 @@
+License (MIT)
+
+Copyright 2026 Chan Zuckerberg Biohub, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,15 +1,20 @@
 ---
 license: mit
+tags:
+- MLM
+- Protein
+- TCR
+- Immunology
 ---
 
-# DecoderTCR v0.1
-DecoderTCR is a protein language model for T-cell receptor (TCR) & peptide-MHC complexes. The model is based on the ESM2 model family.
+# DecoderTCR
+DecoderTCR is a protein language model for T-cell receptor (TCR) & peptide-MHC complexes. The models are based on the ESM2 and ESMC model families.
 
 For Model Code and additional information on installation/usage please see [the associated GitHub repository](https://github.com/czbiohub-chi/DecoderTCR)
 
 ## Model Architecture
 
-DecoderTCR is built on a Transformer-based protein language model (ESM2 family).
+DecoderTCR is built on a Transformer-based protein language model (ESM2 and ESMC families).
 
 ### Core Architecture
 
@@ -39,12 +44,17 @@ The model follows the **ESM2** architecture, a deep Transformer encoder designed
 The model is initialized from a pretrained **ESM2 checkpoint** and further trained via continual pretraining with MLM objectives.
 
 
-### Model Scale (Example Configurations)
+### Released Models
 
-  | Model Variant | Parameters | Layers | Hidden Dim | Attention Heads |
-| --- | --- | --- | --- | --- |
-| ESM2-650M | ~650M | 33 | 1280 | 20 |
-| ESM2-3B | ~3B | 36 | 2560 | 40 |
+This repository hosts two model lines. The **V0.3 ESMC** line (`DecoderTCR-ESMC`) is the current default; the **V0.1 ESM2** line is retained for paper reproduction.
+
+| Model | File | Backbone | Parameters | Layers | Hidden Dim | Attention Heads |
+| --- | --- | --- | --- | --- | --- | --- |
+| DecoderTCR-ESMC 300M | `DecoderTCR-ESMC-V0.3/300M.ckpt` | ESMC | ~300M | 30 | 960 | 15 |
+| DecoderTCR-ESMC 600M (default) | `DecoderTCR-ESMC-V0.3/600M.ckpt` | ESMC | ~600M | 36 | 1152 | 18 |
+| DecoderTCR-ESMC 6B | `DecoderTCR-ESMC-V0.3/6B.ckpt` | ESMC | ~6B | 80 | 2560 | 40 |
+| DecoderTCR 650M | `DecoderTCR-ESM2-V0.1/650M_DecoderTCR.ckpt` | ESM2 | ~650M | 33 | 1280 | 20 |
+| DecoderTCR 3B | `DecoderTCR-ESM2-V0.1/3B_DecoderTCR.ckpt` | ESM2 | ~3B | 36 | 2560 | 40 |
 
 ### Model Card Authors
 
@@ -172,7 +182,12 @@ Should you have any security or privacy issues or questions related to the servi
 ## Acknowledgements
 
 This model builds upon:
-- **ESM2** by Meta AI (Facebook Research) for the base protein language model
+- **ESM2** by Meta AI (Facebook Research) for the ESM2 base protein language model
+- **ESMC** released by Chan Zuckerberg Biohub (https://github.com/Biohub/esm) for the ESMC base protein language model
 - The broader computational biology and immunology research communities
 
 Special thanks to the developers and contributors of the ESM models and the open-source tools that made this work possible.
+
+## License
+
+DecoderTCR code and all released weights are distributed under the [MIT license](https://github.com/czbiohub-chi/DecoderTCR/blob/main/LICENSE). The base backbones are likewise MIT: ESM2 (Meta AI) and ESMC (Chan Zuckerberg Biohub, https://github.com/Biohub/esm).