理解Top-p核采样的原理和应用,掌握与温度参数配合使用的最佳实践
V_p = smallest V ⊆ V such that Σ P(x) ≥ p x∈V
import openai response = openai.ChatCompletion.create( model="gpt-4-turbo", messages=[{"role": "user", "content": "写一个创意故事"}], temperature=0.8, top_p=0.9, # 核采样参数 max_tokens=500 )
import anthropic client = anthropic.Anthropic() response = client.messages.create( model="claude-3-opus-20240229", messages=[{"role": "user", "content": "分析这段代码"}], temperature=0.5, top_p=0.85, # 更保守的设置 max_tokens=1000 )
def get_optimal_sampling_params(task_type): """根据任务类型返回最优采样参数组合""" params = { "factual": { "temperature": 0.3, "top_p": 0.85, "description": "事实准确性优先" }, "balanced": { "temperature": 0.7, "top_p": 0.9, "description": "平衡准确性和创造性" }, "creative": { "temperature": 0.9, "top_p": 0.95, "description": "创造性优先" }, "experimental": { "temperature": 1.0, "top_p": 0.98, "description": "探索性生成" } } return params.get(task_type, params["balanced"])
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import numpy as np def adaptive_top_p(token_probs, base_p=0.9): """根据概率分布的熵动态调整p值""" # 计算熵 entropy = -np.sum(token_probs * np.log(token_probs + 1e-10)) # 高熵(不确定)时增加p值 # 低熵(确定)时减少p值 if entropy > 3.0: # 高不确定性 return min(base_p + 0.05, 0.99) elif entropy < 1.0: # 高确定性 return max(base_p - 0.1, 0.7) else: return base_p
class SamplingOptimizer: def __init__(self): self.task_patterns = { "code": {"keywords": ["function", "class", "def"], "top_p": 0.85}, "math": {"keywords": ["calculate", "solve", "equation"], "top_p": 0.8}, "story": {"keywords": ["story", "imagine", "creative"], "top_p": 0.95} } def optimize_params(self, prompt): """根据提示词内容优化采样参数""" prompt_lower = prompt.lower() for task, config in self.task_patterns.items(): if any(kw in prompt_lower for kw in config["keywords"]): return {"top_p": config["top_p"], "task": task} return {"top_p": 0.9, "task": "general"}