Update README.md

README.md

@@ -1,9 +1,91 @@
 ---
 tags:
-- model_hub_mixin
+- biology
+- ibm
+- mammal
+- pytorch
+- transformers
+library_name: mammal
 license: apache-2.0
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+## Model Summary
+**MAMMAL (Molecular Aliened Multi-Modal Architect Language)**, a versatile multi-task foundation model that learns from large-scale
+biological datasets (over 2 billion samples) across diverse modalities, including
+proteins, small molecules, and genes. We introduce a query syntax that supports
+a wide range of tasks such as classification, regression, and generation—by combining different modalities and entity types as inputs and/or outputs.
+
+- **Developers:** IBM Research
+- **GitHub Repository:** [TBD](TBD)
+- **Paper:** [TBD](https://arxiv.org/abs/TBD)
+- **Release Date**: Oct ?th, 2024
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+
+## Usage
+
+Using `MAMMAL` requires [TBD](https://github.com/TBD)
+
+```
+pip install TBD
+```
+
+A simple example:
+```python
+import torch
+from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
+from mammal.model import Mammal
+from mammal.keys import *
+
+# Load Model
+model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m")
+
+# Load Tokenizer
+tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m")
+
+# Prepare Input Prompt
+protein_calmodulin = "MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQDGFIDKEDLHDGDGKISFEEFLNLVNKEMTADVDGDGQVNYEEFVTMMTSK"
+protein_calcineurin = "MSSKLLLAGLDIERVLAEKNFYKEWDTWIIEAMNVGDEEVDRIKEFKEDEIFEEAKTLGTAEMQEYKKQKLEEAIEGAFDIFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIRQMWDQNGDWDRIKELKFGEIKKLSAKDTRGTIFIKVFENLGTGVDSEYEDVSKYMLKHQ"
+
+# Create and load sample
+sample_dict = dict()
+# Formatting prompt to match pre-training syntax
+sample_dict[ENCODER_INPUTS_STR] = f"<@TOKENIZER-TYPE=AA><BINDING_AFFINITY_CLASS><SENTINEL_ID_0><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{protein_calmodulin}<SEQUENCE_NATURAL_END><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{protein_calcineurin}<SEQUENCE_NATURAL_END><EOS>"
+
+# Tokenize
+tokenizer_op(
+    sample_dict=sample_dict,
+    key_in=ENCODER_INPUTS_STR,
+    key_out_tokens_ids=ENCODER_INPUTS_TOKENS,
+    key_out_attention_mask=ENCODER_INPUTS_ATTENTION_MASK,
+)
+sample_dict[ENCODER_INPUTS_TOKENS] = torch.tensor(sample_dict[ENCODER_INPUTS_TOKENS])
+sample_dict[ENCODER_INPUTS_ATTENTION_MASK] = torch.tensor(sample_dict[ENCODER_INPUTS_ATTENTION_MASK])
+
+# Generate Prediction
+batch_dict = model.generate(
+    [sample_dict],
+    output_scores=True,
+    return_dict_in_generate=True,
+    max_new_tokens=5,
+)
+
+# Get output
+generated_output = tokenizer_op._tokenizer.decode(batch_dict[CLS_PRED][0])
+print(f"{generated_output=}")
+```
+
+For more advanced usage, see our detailed example at: <LINK>
+
+
+## Citation
+
+If you found our work useful, please consider to give a star to the repo and cite our paper:
+```
+@article{TBD,
+  title={TBD},
+  author={IBM Research Team},
+  jounal={arXiv preprint arXiv:TBD},
+  year={2024}
+}
+```