call example

README.md

@@ -3,4 +3,49 @@ license: other
 license_name: license
 license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE
 ---
-# A fine-tuned version of the Qwen/Qwen1.5-0.5B model, the data set used is alpaca_gpt4_data_zh.json
+# A fine-tuned version of the Qwen/Qwen1.5-0.5B model, the data set used is alpaca_gpt4_data_zh.json
+· Call example
+    ```python
+    import os
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+]
+
+device = "cuda"  # the device to load the model onto
+model_path = os.path.dirname(__file__)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+response = ''
+if __name__ == '__main__':
+
+    while True:
+        # prompt = "Give me a short introduction to large language model."
+        prompt = input("input:")
+        messages.append({"role": "user", "content": prompt})
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=512
+        )
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(response)
+        messages.append({"role": "system", "content": response}, )
+
+    ```