如何用DeepSeek优化代码生成？实战案例分析

from transformers import AutoTokenizer, AutoModelForCausalLM

# 加载模型和分词器
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct")

# 定义用户需求
messages = [
    {'role': 'user', 'content': "write a quick sort algorithm in python."}
]

# 生成代码
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

def fib(n):
    if n <= 2:
        return n
    return fib(n-1) + fib(n-2)

def fib(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    a, b = 0, 1
    for _ in range(2, n+1):
        a, b = b, a + b
    return b

# 生成一个Python爬虫，爬取豆瓣电影Top250，保存到CSV文件