Add README with project details

README.md

@@ -5,6 +5,7 @@ tags:
   - byte-tokenization
   - mobile
   - embedded
+  - onnx
 license: cc-by-nc-4.0
 datasets:
   - custom
@@ -74,10 +75,63 @@ The tokenizer is byte-level, compatible with AutoTokenizer from Hugging Face:
 tokenizer = AutoTokenizer.from_pretrained("ijktech/ByteGPT-small")
 ```
 
+### ONNX
+
+The model is also available in ONNX format, and can be used with the ONNX Runtime:
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+# Create ONNX Runtime session
+ort_session = ort.InferenceSession("model.onnx")
+
+# Helper function to generate text using the ONNX model
+def generate_with_onnx(prompt_ids, max_new_tokens=50, temperature=1.0):
+    input_ids = prompt_ids.clone()
+    
+    for _ in range(max_new_tokens):
+        # Get the last block_size tokens if input is too long
+        if input_ids.shape[1] > model.block_size:
+            input_ids = input_ids[:, -model.block_size:]
+            
+        # Run inference
+        ort_inputs = {
+            'input': input_ids.cpu().numpy()
+        }
+        logits = ort_session.run(None, ort_inputs)[0]
+        
+        # Get predictions for the next token
+        logits = torch.from_numpy(logits)
+        logits = logits[:, -1, :] # Only take the last token's predictions
+        
+        # Apply temperature
+        if temperature != 1.0:
+            logits = logits / temperature
+            
+        # Sample from the distribution
+        probs = torch.nn.functional.softmax(logits, dim=-1)
+        next_token = torch.multinomial(probs, num_samples=1)
+        
+        # Append the new token
+        input_ids = torch.cat([input_ids, next_token], dim=1)
+    
+    return input_ids
+
+# Test the generation
+prompt = "Hello"
+prompt_ids = tok(prompt, return_tensors="pt")["input_ids"]
+generated_ids = generate_with_onnx(prompt_ids)
+generated_text = tok.decode(generated_ids[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+#Generated text: Hello everyone!
+#A dinner is only available for St. Loui
+```
+
 ## 📜 License
 📍 **CC-BY-NC-4.0**: Free for non-commercial use.
 
-💼 **Commercial Use**: Contact IJK Technology Ltd for licensing.
+💼 **Commercial Use**: Contact IJK Technology Ltd for licensing at [james@ijktech.com](mailto:[email protected]).
 
 ## 🛠️ About IJK Technology Ltd
 IJK Technology Ltd (IJKTech) develops innovative machine learning models optimized for on-device inference. Our focus is on efficiency, privacy, and usability across mobile and embedded platforms.