Create README.md

README.md

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+Unlike our encrypted DistilBert, this model’s weights reside on Nesa’s secure server, but the tokenizer is on Hugging Face. You can still use the tokenizer to encode and decode text and then submit it for inference via the Nesa network!
+
+```python
+
+###### Load the Tokenizer
+
+from transformers import AutoTokenizer
+
+hf_token = "<HF TOKEN>"  # Replace with your token
+model_id = "nesaorg/Llama-3.2-1B-Instruct-Encrypted"
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, local_files_only=False)
+```
+
+###### Tokenize and Decode Text
+
+```python
+text = "I'm super excited to join Nesa's Equivariant Encryption initiative!"
+
+# Encode text into token IDs
+token_ids = tokenizer.encode(text)
+print("Token IDs:", token_ids)
+
+# Decode token IDs back to text
+decoded_text = tokenizer.decode(token_ids)
+print("Decoded Text:", decoded_text)
+```
+
+###### Example Output:
+
+```
+Token IDs: [128000, 1495, 1135, 2544, 6705, 284, 2219, 11659, 17098, 22968, 8707, 2544, 3539, 285, 34479]
+Decoded Text: I'm super excited to join Nesa's Equivariant Encryption initiative!
+```