Claude Fable 5 与 Mythos 架构：Anthropic 第四层级模型深度技术解析

# 长上下文推理的内存优化示例
import torch
from transformers import AutoModelForCausalLM

class LongContextOptimizer:
    """优化 100 万 token 上下文的内存使用"""
    
    def __init__(self, model_name: str, max_context: int = 1_000_000):
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.max_context = max_context
        
    def optimize_kv_cache(self, input_ids: torch.Tensor):
        """动态量化 KV Cache"""
        from transformers import BitsAndBytesConfig
        quantization_config = BitsAndBytesConfig(
            load_in_8bit=True,
            llm_int8_threshold=6.0
        )
        return quantization_config
    
    def sliding_window_attention(self, hidden_states, window_size=4096):
        """滑动窗口注意力，降低 O(n²) 复杂度"""
        seq_len = hidden_states.shape[1]
        outputs = []
        
        for start in range(0, seq_len, window_size):
            end = min(start + window_size, seq_len)
            window_hidden = hidden_states[:, start:end]
            window_output = self._compute_attention(window_hidden)
            outputs.append(window_output)
            
        return torch.cat(outputs, dim=1)
    
    def _compute_attention(self, hidden_states):
        """窗口内注意力计算"""
        query = self.model.query_proj(hidden_states)
        key = self.model.key_proj(hidden_states)
        value = self.model.value_proj(hidden_states)
        
        attn_weights = torch.matmul(query, key.transpose(-2, -1))
        attn_weights = torch.softmax(attn_weights, dim=-1)
        
        return torch.matmul(attn_weights, value)

// Claude Fable 5 扩展思考 API 使用示例
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

async function extendedThinkingExample() {
  const response = await client.messages.create({
    model: 'claude-fable-5',
    max_tokens: 16000,
    thinking: {
      type: 'enabled',
      budget_tokens: 10000
    },
    messages: [
      {
        role: 'user',
        content: '解决这个复杂的数学问题：证明黎曼猜想在临界线上的零点分布...'
      }
    ]
  });
  
  const thinkingBlocks = response.content.filter(
    block => block.type === 'thinking'
  );
  const answerBlocks = response.content.filter(
    block => block.type === 'text'
  );
  
  console.log('思考过程:', thinkingBlocks.map(b => b.thinking).join('\n'));
  console.log('最终答案:', answerBlocks.map(b => b.text).join('\n'));
}

// Computer Use API 示例
async function computerUseExample() {
  const response = await client.messages.create({
    model: 'claude-fable-5',
    max_tokens: 1024,
    tools: [{
      type: 'computer_use_20241022',
      name: 'computer',
      display_width_px: 1024,
      display_height_px: 768
    }],
    messages: [
      {
        role: 'user',
        content: '打开浏览器，搜索最新的 AI 新闻，并截图保存'
      }
    ]
  });
  
  for (const block of response.content) {
    if (block.type === 'tool_use') {
      console.log('执行操作:', block.name, block.input);
    }
  }
}

# 法律文档分析示例
import anthropic

def analyze_legal_contract(contract_path: str):
    """分析大型法律合同"""
    client = anthropic.Anthropic()
    
    with open(contract_path, 'r', encoding='utf-8') as f:
        contract_text = f.read()
    
    response = client.messages.create(
        model="claude-fable-5",
        max_tokens=8192,
        messages=[{
            "role": "user",
            "content": f"""请分析以下并购合同，重点关注：
1. 风险条款（对买方不利的条款）
2. 赔偿条款是否充分
3. 竞业禁止条款的合理性
4. 知识产权归属是否清晰

合同内容：
{contract_text}"""
        }]
    )
    
    return response.content

# 代码库理解示例
def analyze_codebase(repo_path: str):
    """理解整个代码库的架构"""
    import os
    
    client = anthropic.Anthropic()
    
    code_files = []
    for root, dirs, files in os.walk(repo_path):
        dirs[:] = [d for d in dirs if d not in ['node_modules', '.git', '__pycache__']]
        for file in files:
            if file.endswith(('.py', '.js', '.ts', '.java', '.go')):
                file_path = os.path.join(root, file)
                with open(file_path, 'r', encoding='utf-8') as f:
                    code_files.append(f"\n# File: {file_path}\n" + f.read())
    
    all_code = "\n".join(code_files)
    if len(all_code) > 4_000_000:
        all_code = all_code[:4_000_000]
    
    response = client.messages.create(
        model="claude-fable-5",
        max_tokens=8192,
        messages=[{
            "role": "user",
            "content": f"""请分析以下代码库：
1. 整体架构设计
2. 核心模块和它们的职责
3. 模块间的依赖关系
4. 潜在的代码质量问题
5. 建议的改进方向

代码库内容：
{all_code}"""
        }]
    )
    
    return response.content

// 分级路由策略示例
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

type ModelTier = 'haiku' | 'opus' | 'fable5';

interface RoutingConfig {
  fallbackModels: ModelTier[];
  maxRetries: number;
  timeoutMs: number;
}

class ModelRouter {
  private config: RoutingConfig = {
    fallbackModels: ['opus', 'haiku'],
    maxRetries: 3,
    timeoutMs: 30000
  };
  
  async routeRequest(userMessage: string): Promise<string> {
    const complexity = await this.estimateComplexity(userMessage);
    const model = this.selectModel(complexity);
    return this.callWithFallback(model, userMessage);
  }
  
  private async estimateComplexity(message: string): Promise<number> {
    let score = 0;
    if (message.length > 10000) score += 3;
    else if (message.length > 5000) score += 2;
    else if (message.length > 1000) score += 1;
    
    const complexKeywords = ['分析', '证明', '设计', '架构', '优化'];
    if (complexKeywords.some(kw => message.includes(kw))) score += 2;
    
    return score;
  }
  
  private selectModel(complexity: number): ModelTier {
    if (complexity >= 5) return 'fable5';
    if (complexity >= 3) return 'opus';
    return 'haiku';
  }
  
  private async callWithFallback(
    model: ModelTier, 
    message: string
  ): Promise<string> {
    const modelsToTry = [model, ...this.config.fallbackModels];
    
    for (const m of modelsToTry) {
      try {
        return await this.callModel(m, message);
      } catch (error) {
        console.warn(`Model ${m} failed, trying next...`);
        continue;
      }
    }
    
    throw new Error('All models failed');
  }
  
  private async callModel(model: ModelTier, message: string): Promise<string> {
    const modelId = {
      'haiku': 'claude-haiku-4-5',
      'opus': 'claude-opus-4',
      'fable5': 'claude-fable-5'
    }[model];
    
    const response = await client.messages.create({
      model: modelId,
      max_tokens: 8192,
      messages: [{ role: 'user', content: message }]
    });
    
    return response.content[0].text;
  }
}