Update README.md


README.md

@@ -82,7 +82,7 @@ This model demonstrates a proof-of-concept for hybrid quantum-classical machine
 
 ![chronos_o1_results_english](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/LNOXKqlOV96HWJzammq2Y.png)
   
-
+![chronos_o1_results](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/wE_sARe9MdeSnwiwe8bq6.png)
 
 **Note**: Performance varies with dataset size and quantum simulation parameters. This is a proof-of-concept demonstrating quantum-classical integration.
 
@@ -116,9 +116,9 @@ from sklearn.metrics.pairwise import cosine_similarity
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-tokenizer = AutoTokenizer.from_pretrained("squ11z1/chronos-1.5B")
+tokenizer = AutoTokenizer.from_pretrained("squ11z1/Chronos-1.5B")
 model = AutoModel.from_pretrained(
-    "squ11z1/chronos-1.5B",
+    "squ11z1/Chronos-1.5B",
     torch_dtype=torch.float16
 ).to(device).eval()
 
@@ -220,5 +220,4 @@ MIT License - See LICENSE file for details
 
 ---
 
-
 **Disclaimer**: This is an experimental proof-of-concept model. Performance and accuracy are not guaranteed for production use cases. The quantum component is currently does not provide quantum advantage over classical methods.