Nous Hermes 4架构解析：开源对齐的新范式

┌─────────────────────────────────────────────┐
│              Hermes 4 架构                    │
├─────────────────────────────────────────────┤
│  ┌─────────┐  ┌──────────┐  ┌────────────┐ │
│  │ MoE路由  │  │ 长上下文  │  │ 工具调用    │ │
│  │ (64专家) │  │ (256K)   │  │ 原生层     │ │
│  └────┬────┘  └────┬─────┘  └─────┬──────┘ │
│       │            │              │         │
│  ┌────▼────────────▼──────────────▼──────┐  │
│  │         统一注意力层                    │  │
│  │  (GQA + 滑动窗口 + 全局token)          │  │
│  └────────────────────────────────────────┘  │
│  ┌────────────────────────────────────────┐  │
│  │         对齐适应层                      │  │
│  │  (Neutral Training + DPO + Constitutional)│  │
│  └────────────────────────────────────────┘  │
└─────────────────────────────────────────────┘

# Hermes 4 工具调用架构（概念示意）
class HermesToolLayer(nn.Module):
    def __init__(self, dim, num_tools=128):
        super().__init__()
        self.tool_router = nn.Linear(dim, num_tools)
        self.tool_embedding = nn.Embedding(num_tools, dim)
        self.arg_encoder = nn.Linear(dim, dim)
        
    def forward(self, hidden_states, available_tools):
        # 计算每个工具的调用概率
        tool_logits = self.tool_router(hidden_states[:, -1, :])
        
        # 只在可用工具上做softmax
        mask = self.create_tool_mask(available_tools)
        tool_probs = F.softmax(tool_logits.masked_fill(~mask, -1e9), dim=-1)
        
        # 选择工具并编码参数
        selected_tool = tool_probs.argmax(dim=-1)
        tool_emb = self.tool_embedding(selected_tool)
        
        return {
            "tool_id": selected_tool,
            "tool_probs": tool_probs,
            "tool_embedding": tool_emb
        }

阶段1: Neutral SFT → 阶段2: Constitutional DPO → 阶段3: Self-Correction

# 数据去偏处理流程
class NeutralDataProcessor:
    def process(self, sft_data):
        neutral_samples = []
        for sample in sft_data:
            # 1. 移除主观判断语句
            cleaned = self.remove_subjective_judgments(sample.response)
            # 2. 统一语气和立场
            cleaned = self.neutralize_tone(cleaned)
            # 3. 保留事实和逻辑，移除价值判断
            cleaned = self.separate_facts_from_values(cleaned)
            
            if self.quality_check(cleaned):
                neutral_samples.append({
                    "instruction": sample.instruction,
                    "response": cleaned,
                    "metadata": {"neutralized": True}
                })
        return neutral_samples

# Constitutional DPO 数据生成
constitution = [
    "回复应当准确、基于事实",
    "回复不应包含有害、歧视性内容",
    "当不确定时应当承认不确定性",
    "回复应当尊重用户的自主决策权",
    "回复不应对主观偏好强加标准答案",
]

def generate_preference_pair(prompt, model_a_response, model_b_response, constitution):
    """根据宪法原则自动判断哪个回复更好"""
    scores_a = evaluate_against_constitution(model_a_response, constitution)
    scores_b = evaluate_against_constitution(model_b_response, constitution)
    
    if sum(scores_a) > sum(scores_b):
        return {"chosen": model_a_response, "rejected": model_b_response}
    else:
        return {"chosen": model_b_response, "rejected": model_a_response}

def self_correction_loop(model, prompt, max_rounds=3):
    response = model.generate(prompt)
    
    for round_idx in range(max_rounds):
        # 模型审查自己的回复
        critique = model.generate(
            f"审查以下回复是否存在问题：\n{response}\n"
            f"检查标准：准确性、完整性、安全性、中立性"
        )
        
        if "无明显问题" in critique:
            break
        
        # 根据批评修正
        response = model.generate(
            f"根据以下反馈修正回复：\n"
            f"原回复：{response}\n反馈：{critique}\n修正版："
        )
    
    return response