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from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer from Hugging Face
model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto", # Automatically selects the appropriate dtype
device_map="auto" # Distributes the model across available devices
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt for the model
prompt = "write a quick sort algorithm."
# Prepare the messages to pass to the model
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
# Generate the input for the model using the tokenizer
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the response from the model
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512 # Limit the length of the generated text
)
# Decode and print the result
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) |