Update README.md

README.md

@@ -44,6 +44,8 @@ This model is fine-tuned using LoRa (Low-Rank Adaptation) on the "Noticias La Ra
 
 This model can be used for **conversational AI tasks** related to Spanish-language news. The fine-tuned LoRa model is especially suitable for use cases that require both understanding and generating text, such as chat-based interactions, answering questions about news, and discussing headlines.
 
+Copy the code from this Gist for easy chating using Jupyter Notebook: https://gist.github.com/reddgr/20c2e3ea205d1fedfdc8be94dc5c1237
+
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
@@ -70,19 +72,19 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Copy the code from this Gist for easy chating using Jupyter Notebook: https://gist.github.com/reddgr/20c2e3ea205d1fedfdc8be94dc5c1237
+
+Additionally, you can use the code below to get started with the model.
 
-```python
+!python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 
-# Load the tokenizer and model
 save_directory = "./fine_tuned_model"
 tokenizer = AutoTokenizer.from_pretrained(save_directory)
 model = AutoModelForCausalLM.from_pretrained(save_directory)
 peft_model = PeftModel.from_pretrained(model, save_directory)
 
-# Example usage
 input_text = "¿Qué opinas de las noticias recientes sobre la economía?"
 inputs = tokenizer(input_text, return_tensors="pt")
 output = peft_model.generate(**inputs, max_length=50)