2ca77d0aee
- ADR-0001: Multi-Agent Coworking Architecture with LLM Gateway Orchestrator - ADR-0002: Tier Assignment Strategy for Model Selection (cost-first escalation) - ADR-0003: Confidence Gate Thresholds & Learning Cycle Intervals (6h/12h/24h cycles) - ADR-0004: External Provider Fallback Chain Ordering (Cerebras → Groq → Mistral) - Enhanced client SDK: Offline Ollama fallback, health checks, exponential backoff retry - Integration tests: claude-code-integration.test.ts (14 test cases) - PHASE_2F_DEPLOYMENT.md: Pre-deployment checklist, automated deploy, rollback plan - Post-deployment verification procedures for health, client fallback, metrics
51 lines
1.5 KiB
YAML
51 lines
1.5 KiB
YAML
database_url: "postgresql://llm:llm_secure_2026@127.0.0.1:15432/llm_gateway"
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gateway_url: "https://llm-gateway.context-x.org"
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ollama_url: "http://localhost:11434"
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models:
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qwen_14b_hf: "Qwen/Qwen2.5-14B-Instruct" # HuggingFace model ID — used for general fine-tuning
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qwen_7b_hf: "Qwen/Qwen2.5-7B-Instruct" # For task-specific runs (smaller, faster)
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training:
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device: "mps" # Apple Silicon MPS — fallback to "cpu" if MPS unavailable
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max_seq_length: 2560 # BlogLLM ultra: 2560 for full article coverage + aggressive LoRA
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lora_r: 32 # Doubled from 16 for domain specialization
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lora_alpha: 64 # Doubled from 32 for stronger adaptation
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lora_dropout: 0.05
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target_modules:
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- "q_proj"
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- "k_proj"
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- "v_proj"
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- "o_proj"
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- "gate_proj"
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- "up_proj"
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- "down_proj"
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sft:
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num_epochs: 15 # Ultra-aggressive: 15 epochs for blog domain specialization (vs standard 3)
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batch_size: 1
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gradient_accumulation: 8
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learning_rate: 2.0e-4
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warmup_ratio: 0.1
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dpo:
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num_epochs: 1
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batch_size: 1
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gradient_accumulation: 4
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beta: 0.1 # DPO temperature — higher = more conservative
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learning_rate: 5.0e-5
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evaluation:
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min_improvement_to_deploy: 0.3 # confidence delta required before deployment
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n_eval_samples: 20
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output:
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adapters_dir: "adapters"
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models_dir: "models"
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llama_cpp:
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convert_script: "/opt/homebrew/Cellar/llama.cpp/8680/bin/convert_hf_to_gguf.py"
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quantize_binary: "/opt/homebrew/bin/llama-quantize"
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python_bin: "/opt/homebrew/bin/python3.13"
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default_quantization: "Q5_K_M"
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