Create CodeQwen1.5-base-Chat.py

examples/CodeQwen1.5-base-Chat.py

@@ -0,0 +1,36 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda" # the device to load the model onto
+
+# Now you do not need to add "trust_remote_code=True"
+tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
+model = AutoModelForCausalLM.from_pretrained("Qwen/CodeQwen1.5-7B-Chat", device_map="auto").eval()
+
+# tokenize the input into tokens
+
+# Instead of using model.chat(), we directly use model.generate()
+# But you need to use tokenizer.apply_chat_template() to format your inputs as shown below
+prompt = "write a quick sort algorithm."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"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)
+
+# Directly use generate() and tokenizer.decode() to get the output.
+# Use `max_new_tokens` to control the maximum output length.
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048
+)
+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)