Update README.md

README.md

@@ -22,7 +22,7 @@ datasets:
 </p>
 
 <h1 align="center"> Precious3GPT </h1>
-<h3 align="center"> A multimodal multi-omics multi-species multi-tissue language model. </h3>
+<h3 align="center"> A multimodal multi-omics multi-species multi-tissue Large Language of Life Model (LLLM/LLoLM). </h3>
 <p align="center">
   📃 <a href="https://doi.org/10.1101/2024.07.25.605062" target="_blank">Pre-print</a> • 👾 <a href="https://discord.gg/P4PWFNbYFg" target="_blank">Discord bot</a> • 🧬 <a href="https://insilico.com/repository/precious3gpt" target="_blank">Validation digest</a>
   <br>
@@ -40,7 +40,7 @@ datasets:
 
 <h1 align="center"> Model summary </h1>
 
-- Precious3GPT (P3GPT) is a unique language model that has been trained on 1.2MM omics data points, knowledge graphs, and biomedical texts (PubMed) to be used in drug discovery and aging research;
+- Precious3GPT (P3GPT) is a unique Large Language of Life Model (LLLM/LLoLM) that has been trained on 1.2MM omics data points, knowledge graphs, and biomedical texts (PubMed) to be used in drug discovery and aging research;
 
 - P3GPT simulates biological processes on an omics level to return the transcriptomic, epigenetic, or proteomic signatures of a wide variety of perturbators;
 
@@ -50,6 +50,13 @@ datasets:
 
 - You may work with P3GPT either by downloading model weights for a local deployment or by interacting with the Discord bot on the official Inisilico Medicine's server.
 
+- Download the model with:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("insilicomedicine/precious3-gpt-multi-modal", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("insilicomedicine/precious3-gpt-multi-modal", trust_remote_code=True)
+```
+
 <h1 align="center"> Model usage guide </h1>
 
 ### Run model with an endpoint