Qwen2.5-Coder.py

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/Qwen2.5-Coder-32B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-32B", device_map="auto").eval()


# tokenize the input into tokens
input_text = "#write a quick sort algorithm"
model_inputs = tokenizer([input_text], return_tensors="pt").to(device)

# Use `max_new_tokens` to control the maximum output length.
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=1024, do_sample=False)[0]
# The generated_ids include prompt_ids, so we only need to decode the tokens after prompt_ids.
output_text = tokenizer.decode(generated_ids[len(model_inputs.input_ids[0]):], skip_special_tokens=True)

print(f"Prompt: {input_text}\n\nGenerated text: {output_text}")