add to README model description (#1)

- add to README model description (4f11fe3e11e1092d47f8c22bdbc7e9a269d61f3d)


Co-authored-by: Moshe Raboh <[email protected]>

README.md

@@ -1,32 +1,42 @@
 ---
 tags:
 - biology
+- small-moelcule
+- single-cell-genes
 - ibm
 - mammal
 - pytorch
 - transformers
-library_name: mammal
+library_name: biomed
 license: apache-2.0
 ---
 
+The **ibm/biomed.omics.bl.sm.ma-ted-400m** model is a biomedical foundation model trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data.  
+Designed for robust performance, it achieves state-of-the-art results over a variety of tasks across the entire drug discovery pipeline and the diverse biomedical domains.  
+
+Based on the **M**olecular **A**ligned **M**ulti-**M**odal **A**rchitecture and **L**anguage (**MAMMAL**), this model introduces a flexible, multi-domain architecture with an adaptable task prompt syntax.  
+The syntax allows for dynamic combinations of tokens and scalars, enabling classification, regression, and generation tasks either within a single domain or with cross-domain entities.
+
+**TBD: add main paper figure when ready**
+
 ## Model Summary
 
 - **Developers:** IBM Research
-- **GitHub Repository:** [TBD](TBD)
-- **Paper:** [TBD](https://arxiv.org/abs/TBD)
-- **Release Date**: Oct ?th, 2024
+- **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
+- **Paper:** TBD
+- **Release Date**: Oct 28th, 2024
 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
 
 
 ## Usage
 
-Using `MAMMAL` requires [TBD](https://github.com/TBD)
+Using `ibm/biomed.omics.bl.sm.ma-ted-400m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
 
 ```
-pip install TBD
+pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
 ```
 
-A simple example:
+A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-400m`:
 ```python
 import torch
 from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp