Update README.md

README.md

@@ -1,3 +1,55 @@
----
-license: mit
----
+---
+license: apache-2.0
+tags:
+- pretrained
+- mistral
+- chemistry
+---
+
+# Model Card for Mistral-Chem-v1-15M (Mistral for protein)
+
+The Mistral-Chem-v1-15M Large Language Model (LLM) is a pretrained generative chemical molecule model with 1.9M parameters x 8 experts = 15.2M parameters. 
+It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. 
+The model was pretrained using 10M molecule SMILES strings from the ZINC 15 database. 
+
+## Model Architecture
+
+Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices:
+- Grouped-Query Attention
+- Sliding-Window Attention
+- Byte-fallback BPE tokenizer
+- Mixture of Experts
+
+## Load the model from huggingface:
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Chem-v1-15M", trust_remote_code=True) 
+model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Chem-v1-15M", trust_remote_code=True)
+```
+
+## Calculate the embedding of a DNA sequence
+
+```
+chem = "CCCCC[C@H](Br)CC"
+inputs = tokenizer(chem, return_tensors = 'pt')["input_ids"]
+hidden_states = model(inputs)[0] # [1, sequence_length, 256]
+
+# embedding with max pooling
+embedding_max = torch.max(hidden_states[0], dim=0)[0]
+print(embedding_max.shape) # expect to be 256
+```
+
+## Troubleshooting
+
+Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
+
+## Notice
+
+Mistral-Chem-v1-15M is a pretrained base model for chemistry.
+
+## Contact
+ 
+Raphaël Mourad. [email protected]