Update README.md

README.md

@@ -38,7 +38,7 @@ Remarkably, this model was trained on a single A100 GPU with limited computation
 
 ## How to Get Started with the Model
 
-- To load model and tokenizer:
+### To load the model and tokenizer:
 
 ```python
 from transformers import (
@@ -47,7 +47,7 @@ from transformers import (
 )
 import torch
 
-model_name = "ignita/RigoChat-7b-v2"
+model_name = "IIC/RigoChat-7b-v2"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -58,8 +58,35 @@ model = AutoModelForCausalLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained(
     model_name,
     trust_remote_code=True
-))))))))))))))))))))))
+)
+```
+
+### Sample generation
+
+```python
+messages = [
+    {"role": "user", "content": "¿Cómo puedo transformar un diccionario de listas en una lista de diccionarios en Python?"}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
+### Tool Use
 
 ## Training Details