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add to README model description

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  ---
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  tags:
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  - biology
 
 
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  - ibm
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  - mammal
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  - pytorch
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  - transformers
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- library_name: mammal
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  license: apache-2.0
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  ---
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  ## Model Summary
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  - **Developers:** IBM Research
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- - **GitHub Repository:** [TBD](TBD)
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- - **Paper:** [TBD](https://arxiv.org/abs/TBD)
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- - **Release Date**: Oct ?th, 2024
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  - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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  ## Usage
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- Using `MAMMAL` requires [TBD](https://github.com/TBD)
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  ```
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- pip install TBD
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  ```
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- A simple example:
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  ```python
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  import torch
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  from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
 
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  ---
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  tags:
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  - biology
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+ - small-moelcule
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+ - single-cell-genes
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  - ibm
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  - mammal
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  - pytorch
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  - transformers
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+ library_name: biomed
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  license: apache-2.0
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  ---
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+ 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.
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+ 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.
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+
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+ 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.
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+ 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.
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+
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+ **TBD: add main paper figure when ready**
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+
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  ## Model Summary
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  - **Developers:** IBM Research
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+ - **GitHub Repository:** https://github.com/BiomedSciAI/biomed-multi-alignment
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+ - **Paper:** TBD
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+ - **Release Date**: Oct 28th, 2024
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  - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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  ## Usage
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+ Using `ibm/biomed.omics.bl.sm.ma-ted-400m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
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  ```
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+ pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
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  ```
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+ A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-400m`:
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  ```python
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  import torch
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  from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp