Update README.md

README.md

@@ -33,18 +33,18 @@ In this version, we employed our new, improved decomposable ELM techniques on a
 
 ## 1. Run ELM Turbo models with Huggingface Transformers library.
 There are three ELM Turbo slices derived from the `Meta-Llama-3.1-8B-Instruct` model: 
-  1. `slicexai/Llama3.1-elm-turbo-3B-instruct` (3B params)
+  1. **`slicexai/Llama3.1-elm-turbo-3B-instruct` (3B params)**
   2. `slicexai/Llama3.1-elm-turbo-4B-instruct`(4B params)
   3. `slicexai/Llama3.1-elm-turbo-6B-instruct` (6B params) 
 
 Make sure to update your transformers installation via pip install --upgrade transformers.
 
-Example - To run the `slicexai/Llama3.1-elm-turbo-4B-instruct`
+Example - To run the `slicexai/Llama3.1-elm-turbo-3B-instruct`
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import torch
 
-elm_turbo_model = "slicexai/Llama3.1-elm-turbo-4B-instruct"
+elm_turbo_model = "slicexai/Llama3.1-elm-turbo-3B-instruct"
 model = AutoModelForCausalLM.from_pretrained( 
     elm_turbo_model,  
     device_map="cuda",