Update README.md

README.md

@@ -70,4 +70,67 @@ language:
 - xh
 - yo
 - zu
----
+---
+
+
+## Usage
+
+### ONNXRuntime
+
+```py
+from transformers import AutoConfig, AutoTokenizer
+import onnxruntime
+import numpy as np
+
+# 1. Load config, processor, and model
+model_id = "./path/to/model/"
+config = AutoConfig.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model_path = f"{model_id}/onnx/model.onnx"
+decoder_session = onnxruntime.InferenceSession(model_path)
+
+## Set config values
+num_key_value_heads = config.num_key_value_heads
+head_dim = config.hidden_size // config.num_attention_heads
+num_hidden_layers = config.num_hidden_layers
+eos_token_id = config.eos_token_id
+
+# 2. Prepare inputs
+messages = [{"role": "user", "content": "Explica en español qué significa la palabra japonesa 'ikigai' y da un ejemplo práctico."}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="np")
+input_ids = inputs['input_ids']
+attention_mask = inputs['attention_mask']
+batch_size = input_ids.shape[0]
+past_key_values = {
+    f'past_key_values.{layer}.{kv}': np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32)
+    for layer in range(num_hidden_layers)
+    for kv in ('key', 'value')
+}
+
+# 3. Generation loop
+max_new_tokens = 1024
+generated_tokens = np.array([[]], dtype=np.int64)
+for i in range(max_new_tokens):
+  logits, *present_key_values = decoder_session.run(None, dict(
+      input_ids=input_ids,
+      attention_mask=attention_mask,
+      **past_key_values,
+  ))
+
+  ## Update values for next generation loop
+  input_ids = logits[:, -1].argmax(-1, keepdims=True)
+  attention_mask = np.concatenate([attention_mask, np.ones_like(input_ids, dtype=np.int64)], axis=-1)
+  for j, key in enumerate(past_key_values):
+    past_key_values[key] = present_key_values[j]
+
+  generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1)
+  if np.isin(input_ids, eos_token_id).any():
+    break
+
+  ## (Optional) Streaming
+  print(tokenizer.decode(input_ids[0]), end='', flush=True)
+print()
+
+# 4. Output result
+print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
+```