Fix YAML metadata - Add proper model card frontmatter

README.md

@@ -1,3 +1,23 @@
+---
+language: en
+license: mit
+tags:
+- token-efficiency
+- transformer
+- dynamic-allocation
+- scaling-laws
+- information-theoretic
+- efficiency-breakthrough
+- compact-ai
+- production-ready
+- dynamic-computation
+widget:
+- text: "Hello, world! This is a test of our token-efficient model."
+- text: "Explain quantum computing in simple terms."
+- text: "Write a short story about AI and efficiency."
+- text: "The company's quarterly earnings exceeded expectations by 15%."
+---
+
 # 🚀 Token Efficiency Breakthrough: Compact AI Model
 
 ## 📊 Achievement Summary
@@ -37,8 +57,8 @@ Instead, we must move to information-theoretic optimization approaches like dyna
 from transformers import AutoTokenizer, AutoModel
 
 # Load our efficient model
-tokenizer = AutoTokenizer.from_pretrained("compact-ai/token-efficiency-breakthrough")
-model = AutoModel.from_pretrained("compact-ai/token-efficiency-breakthrough")
+tokenizer = AutoTokenizer.from_pretrained("likhonsheikh/token-efficiency-breakthrough")
+model = AutoModel.from_pretrained("likhonsheikh/token-efficiency-breakthrough")
 
 # Your text processing code
 inputs = tokenizer("Your text here", return_tensors="pt")
@@ -50,8 +70,8 @@ outputs = model(**inputs)
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-tokenizer = AutoTokenizer.from_pretrained("compact-ai/token-efficiency-breakthrough")
-model = AutoModel.from_pretrained("compact-ai/token-efficiency-breakthrough")
+tokenizer = AutoTokenizer.from_pretrained("likhonsheikh/token-efficiency-breakthrough")
+model = AutoModel.from_pretrained("likhonsheikh/token-efficiency-breakthrough")
 
 def process_with_efficiency(text):
     inputs = tokenizer(text, return_tensors="pt")