Update README.md

README.md

@@ -1,3 +1,56 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- pretrained
+- mistral
+- DNA
+- codon
+---
+
+# Model Card for Mistral-Codon-v1-1M (Mistral for coding DNA)
+
+The Mistral-Codon-v1-1M Large Language Model (LLM) is a pretrained generative DNA sequence model with 1M parameters. 
+It is derived from Mixtral-8x7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced. 
+The model was pretrained using 24M coding DNA sequences (3000bp) from many different species (vertebrates, plants, bacteria, viruses, ...). 
+
+## 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-Codon-v1-1M", trust_remote_code=True) 
+model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Codon-v1-1M", trust_remote_code=True)
+```
+
+## Calculate the embedding of a coding sequence
+
+```
+codon_dna = "TGA TGA TTG GCG CGG CTA GGA TCG GCT"
+inputs = tokenizer(codon_dna, 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-Codon-v1-1M is a pretrained base model for coding DNA.
+
+## Contact
+ 
+Raphaël Mourad. raphael.mourad@univ-tlse3.fr