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

DecoderTCR-C-V0.3/300M.ckpt

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DecoderTCR-C-V0.3/600M.ckpt

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

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

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DecoderTCR-ESM2-V0.1/650M_DecoderTCR.ckpt

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LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Chan Zuckerberg Biohub
+
+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

@@ -2,14 +2,14 @@
 license: mit
 ---
 
-# 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 ESM-2 and ESM-C 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 (ESM-2 and ESM-C families).
 
 ### Core Architecture
 
@@ -39,12 +39,17 @@ 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 ESM-C** line (`DecoderTCR-C`) is the current default; the **V0.1 ESM-2** line is retained for paper reproduction.
+
+| Model | File | Backbone | Parameters | Layers | Hidden Dim | Attention Heads |
+| --- | --- | --- | --- | --- | --- | --- |
+| DecoderTCR-C 300M | `DecoderTCR-C-V0.3/300M.ckpt` | ESM-C | ~300M | 30 | 960 | 15 |
+| DecoderTCR-C 600M (default) | `DecoderTCR-C-V0.3/600M.ckpt` | ESM-C | ~600M | 36 | 1152 | 18 |
+| DecoderTCR-C 6B | `DecoderTCR-C-V0.3/6B.ckpt` | ESM-C | ~6B | 80 | 2560 | 40 |
+| DecoderTCR 650M | `DecoderTCR-ESM2-V0.1/650M_DecoderTCR.ckpt` | ESM-2 | ~650M | 33 | 1280 | 20 |
+| DecoderTCR 3B | `DecoderTCR-ESM2-V0.1/3B_DecoderTCR.ckpt` | ESM-2 | ~3B | 36 | 2560 | 40 |
 
 ### Model Card Authors
 
@@ -172,7 +177,12 @@ 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
+- **ESM-2** by Meta AI (Facebook Research) for the ESM-2 base protein language model
+- **ESM-C** released by Chan Zuckerberg Biohub (https://github.com/Biohub/esm) for the ESM-C 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: ESM-2 (Meta AI) and ESM-C (Chan Zuckerberg Biohub, https://github.com/Biohub/esm).