bb4922adeb
Add DPOQualityValidator that catches bad training pairs before they enter the tightening loop. Wired into DPOPairGenerator between generate() and export() as an automatic quality gate. New module: dpo_quality.py - 5 single-pair quality checks: 1. Field length minimums (prompt ≥40, chosen ≥80, rejected ≥30 chars) 2. Chosen/rejected length ratio (chosen must be ≥1.3x longer) 3. Chosen≈rejected similarity (Jaccard ≤0.70 — catches low-contrast) 4. Vocabulary diversity in chosen (unique word ratio ≥0.30) 5. Substance markers in chosen (≥2 fleet/training/action terms) - 2 cross-pair quality checks: 6. Near-duplicate prompts within batch (Jaccard ≤0.85) 7. Cross-run dedup against recent JSONL history files - Two modes: 'drop' (filter out bad pairs) or 'flag' (export with warning) - BatchReport with per-pair diagnostics, pass rates, and warnings - Standalone CLI: python3 dpo_quality.py <file.jsonl> [--strict] [--json] Modified: dpo_generator.py - Imports DPOQualityValidator with graceful degradation - Initializes from config validation section (enabled by default) - Validates between generate() and export() in run() - Quality report included in pipeline result dict - Validator failure never blocks — falls back to unvalidated export Modified: config.yaml - New deepdive.training.dpo.validation section with all tunable knobs: enabled, flagged_pair_action, similarity thresholds, length minimums, dedup_history_files Integration tested — 6 test cases covering: ✓ Good pairs pass (3/3 accepted) ✓ Bad pairs caught: too-short, high-similarity, inverted signal (0/3) ✓ Near-duplicate prompt detection (1/2 deduped) ✓ Flag mode preserves pairs with warnings (3/3 flagged) ✓ Cross-run deduplication against history (1 dupe caught) ✓ Full generator→validator→export pipeline (6/6 validated)
134 lines
3.8 KiB
YAML
134 lines
3.8 KiB
YAML
# Deep Dive Configuration
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# Copy to config.yaml and customize
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deepdive:
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# Schedule
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schedule:
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daily_time: "06:00"
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timezone: "America/New_York"
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# Phase 1: Aggregation
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sources:
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- name: "arxiv_cs_ai"
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url: "http://export.arxiv.org/rss/cs.AI"
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type: "rss"
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fetch_window_hours: 24
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max_items: 50
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- name: "arxiv_cs_cl"
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url: "http://export.arxiv.org/rss/cs.CL"
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type: "rss"
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fetch_window_hours: 24
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max_items: 50
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- name: "arxiv_cs_lg"
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url: "http://export.arxiv.org/rss/cs.LG"
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type: "rss"
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fetch_window_hours: 24
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max_items: 50
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- name: "openai_blog"
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url: "https://openai.com/blog/rss.xml"
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type: "rss"
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fetch_window_hours: 48
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max_items: 5
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- name: "anthropic_blog"
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url: "https://www.anthropic.com/blog/rss.xml"
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type: "rss"
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fetch_window_hours: 48
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max_items: 5
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- name: "deepmind_blog"
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url: "https://deepmind.google/blog/rss.xml"
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type: "rss"
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fetch_window_hours: 48
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max_items: 5
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# Phase 2: Relevance
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relevance:
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model: "all-MiniLM-L6-v2" # ~80MB embeddings model
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top_n: 10 # Items selected for briefing
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min_score: 0.25 # Hard cutoff
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keywords:
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- "LLM agent"
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- "agent architecture"
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- "tool use"
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- "function calling"
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- "chain of thought"
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- "reasoning"
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- "reinforcement learning"
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- "RLHF"
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- "GRPO"
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- "PPO"
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- "fine-tuning"
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- "transformer"
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- "attention mechanism"
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- "inference optimization"
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- "quantization"
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- "local LLM"
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- "llama.cpp"
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- "ollama"
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- "vLLM"
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- "Hermes"
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- "open source AI"
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# Phase 3: Synthesis
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synthesis:
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llm_endpoint: "http://localhost:4000/v1" # Local llama-server
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llm_model: "gemma4:12b"
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max_summary_length: 800
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temperature: 0.7
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# Phase 4: Audio
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tts:
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engine: "piper"
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model_path: "~/.local/share/piper/models"
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voice: "en_US-amy-medium"
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speed: 1.0
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output_format: "mp3" # piper outputs WAV, convert for Telegram
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# Phase 3.5: DPO Training Pair Generation
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training:
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dpo:
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enabled: true
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output_dir: "~/.timmy/training-data/dpo-pairs"
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min_score: 0.5 # Only generate pairs from items above this relevance score
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max_pairs_per_run: 30 # Cap pairs per pipeline execution
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pair_types: # Which pair strategies to use
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- "summarize" # Paper summary → fleet-grounded analysis
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- "relevance" # Relevance analysis → scored fleet context
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- "implication" # Implications → actionable insight
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validation:
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enabled: true
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flagged_pair_action: "drop" # "drop" = remove bad pairs, "flag" = export with warning
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min_prompt_chars: 40 # Minimum prompt length
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min_chosen_chars: 80 # Minimum chosen response length
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min_rejected_chars: 30 # Minimum rejected response length
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min_chosen_rejected_ratio: 1.3 # Chosen must be ≥1.3x longer than rejected
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max_chosen_rejected_similarity: 0.70 # Max Jaccard overlap between chosen/rejected
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max_prompt_prompt_similarity: 0.85 # Max Jaccard overlap between prompts (dedup)
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dedup_history_files: 5 # How many recent JSONL files to scan for cross-run dedup
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# Phase 0: Fleet Context Grounding
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fleet_context:
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enabled: true
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gitea_url: "https://forge.alexanderwhitestone.com"
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token: "${GITEA_TOKEN}" # From environment
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owner: "Timmy_Foundation"
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repos:
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- "timmy-config"
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- "the-nexus"
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- "timmy-home"
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- "hermes-agent"
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# Phase 5: Delivery
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delivery:
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method: "telegram"
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bot_token: "${TELEGRAM_BOT_TOKEN}" # From env
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channel_id: "-1003664764329"
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send_text_summary: true
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output_dir: "~/briefings"
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log_level: "INFO"
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