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
104 lines
4.3 KiB
YAML
104 lines
4.3 KiB
YAML
# ═══════════════════════════════════════════════════════════════
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# fixes-training.yaml — Training Config for MAGATAMA Operations AI
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# Dataset: 254 fixes from production (fixes.json → 4 JSONL formats)
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# ═══════════════════════════════════════════════════════════════
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# Data source (choose one)
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data:
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sft_file: "data/fixes-chatml-sft.jsonl" # ChatML format for PEFT/TRL
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conversational_file: "data/fixes-conversational-sft.jsonl" # messages[] format
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alpaca_file: "data/fixes-alpaca.jsonl" # instruction/input/output
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dpo_file: "data/fixes-dpo-pairs.jsonl" # prompt/chosen/rejected
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# Dataset stats (auto-generated 2026-04-13)
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total_samples: 254
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avg_tokens_per_sample: 402
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coverage_at_512: "94%"
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coverage_at_1024: "100%"
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# Model selection
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models:
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# Primary: Qwen2.5-7B for ops knowledge (smaller, faster training)
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primary: "Qwen/Qwen2.5-7B-Instruct"
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# Secondary: Qwen2.5-14B for deeper reasoning (if VRAM allows)
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secondary: "Qwen/Qwen2.5-14B-Instruct"
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# SFT Training (Phase 1)
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sft:
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device: "mps" # Apple Silicon MPS
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max_seq_length: 1024 # Covers 100% of samples (avg 402 tokens)
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lora_r: 16
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lora_alpha: 32
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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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num_epochs: 3
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batch_size: 1 # MPS: batch_size=1 required
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gradient_accumulation: 8 # Effective batch = 8
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learning_rate: 2.0e-4
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warmup_ratio: 0.1
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weight_decay: 0.01
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lr_scheduler: "cosine"
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logging_steps: 10
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save_steps: 50
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eval_steps: 50
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eval_split: 0.1 # 10% = ~25 samples for eval
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# MPS-specific
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gradient_checkpointing: false # Not supported on MPS
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bf16: false # MPS doesn't support bf16
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fp16: false # Use fp32 on MPS
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use_cache: false # Required: false during training
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# DPO Training (Phase 2 — after SFT)
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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
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learning_rate: 5.0e-5
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max_seq_length: 1024
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# Evaluation
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evaluation:
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n_eval_samples: 25 # 10% of 254
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min_improvement_to_deploy: 0.3 # Confidence delta required
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eval_prompts:
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- "System: proxmox | Component: pvestatd\nSymptome:\n- Proxmox GUI: alle Graphs leer\n- pvestatd restart schlaegt fehl"
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- "System: llm-gateway | Component: free-llm-fallback\nSymptome:\n- Ollama nicht erreichbar\n- Kein LLM-Fallback"
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- "System: shieldx | Component: cipher-bypass\nSymptome:\n- ROT13/Base64 Injections nicht erkannt"
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- "System: peercortex | Component: api-ratelimit\nSymptome:\n- HTTP 429 Too Many Requests\n- Hunderte parallele Requests"
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- "System: ctxevent | Component: database-auth\nSymptome:\n- SASL: SCRAM-SERVER-FIRST-MESSAGE error"
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# Output
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output:
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adapters_dir: "adapters/fixes-ops-ai"
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gguf_quantization: "Q5_K_M" # Best quality/size ratio for 7B
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ollama_model_name: "magatama-ops"
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ollama_tag: "fixes-v1"
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# Training command reference:
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# ═══════════════════════════════════════════════════════════════
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# Phase 1 — SFT:
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# cd packages/fine-tuner
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# python3 scripts/manual_trigger.py --task-type fixes --config config/fixes-training.yaml
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#
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# Phase 2 — DPO (after SFT):
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# python3 scripts/manual_trigger.py --dpo --config config/fixes-training.yaml
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#
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# Phase 3 — Convert to GGUF + deploy to Ollama:
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# python3 -m src.converter --adapter adapters/fixes-ops-ai --quantize Q5_K_M
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# ollama create magatama-ops:fixes-v1 -f adapters/fixes-ops-ai/Modelfile
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#
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# Estimated training time on Mac Studio M4 Max 48GB:
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# SFT 7B: ~15-20 min (254 samples × 3 epochs, ~762 steps)
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# SFT 14B: ~45-60 min
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# DPO: ~5-10 min (1 epoch)
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# ═══════════════════════════════════════════════════════════════
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