4c54a6fa92
Complete code quality audit of llm-gateway pipeline modules for MAGATAMA standard compliance (50-line function maximum). All pipeline functions refactored to ensure high cohesion and readability. Pipeline module compliance (verified): ✅ llm-client.ts — Refactored callOllama() (58→26 lines) via helper extraction ✅ instrumented-llm-client.ts — All functions <50 lines (wrapper layer) ✅ router.ts — Refactored routeByScore() (81→32 lines) via delegation ✅ request-scorer.ts — 870-line file, all functions <50 lines ✅ external-providers.ts — All functions <50 lines (49-line max) ✅ post-validator.ts — All validators <50 lines Verified: ✓ npm run build (TypeScript, zero errors) ✓ All 6 pipeline modules independently audited ✓ Production-ready for Erik deployment (PM2 ids 19+20, port 3103) Deployment target: Gitea (192.168.178.196:3000/rene/llm-gateway)
40 lines
1.3 KiB
YAML
40 lines
1.3 KiB
YAML
database_url: "${DATABASE_URL}" # Set via environment variable at runtime
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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_7b_hf: "Qwen/Qwen2.5-7B-Instruct" # fo-blog uses 7B — fast inference on Mac Studio
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training:
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device: "mps" # Apple Silicon MPS
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max_seq_length: 2048 # Reduced from 2560 — articles avg 7k chars, fits in 2k tokens
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lora_r: 16 # Halved from 32 — less aggressive, prevents memorization
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lora_alpha: 32 # Halved from 64 — proportional to r
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lora_dropout: 0.1 # Increased from 0.05 — adds regularization
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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: 3 # Down from 15 — 3 epochs is standard for SFT, prevents overfitting
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batch_size: 1
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gradient_accumulation: 8
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learning_rate: 5.0e-5 # Down from 2e-4 — gentler updates preserve base model
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warmup_ratio: 0.05
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output:
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adapters_dir: "adapters"
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models_dir: "models"
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model_name: "fo-blog-v6"
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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: "Q4_K_M" # Smaller than Q5_K_M, still high quality
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