Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- pretrained
+- mistral
+- DNA
+- non coding
+- rfam
+- biology
+- genomics
+---
+
+# Model Card for Mistral-DNA-v1-138M-noncoding (mistral for DNA)
+
+The Mistral-DNA-v1-138M-noncoding Large Language Model (LLM) is a pretrained generative DNA text model with 17.31M parameters x 8 experts = 138.5M parameters. 
+It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced. 
+The model was pretrained using around 1940736 non coding RNAs > 100b from rfam database. Sequences were split into 100b sequences.
+
+NB: the DNA sequence was used, not the RNA sequence.  
+
+For full details of this model please read our [github repo](https://github.com/raphaelmourad/Mistral-DNA).
+
+## Model Architecture
+
+Like Mistral-7B-v0.1, it is a transformer model, with the following architecture choices:
+- Grouped-Query Attention
+- Sliding-Window Attention
+- Byte-fallback BPE tokenizer
+
+## Load the model from huggingface:
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-noncoding", trust_remote_code=True) # Same as DNABERT2
+model = AutoModel.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-noncoding", trust_remote_code=True)
+```
+
+## Calculate the embedding of a DNA sequence
+
+```
+dna = "TGATGATTGGCGCGGCTAGGATCGGCT"
+inputs = tokenizer(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-DNA-v1-138M-noncoding is a pretrained base model for non coding RNAs.
+
+## Contact
+ 
+Raphaël Mourad. [email protected]
+