docs: merge tooling findings into SYNTHESIS/GOTCHAS/CORPUS_* and add handoff
Patches the top-level corpus docs with the 13 findings flagged during the 2026-04-18 canonical tooling research pass. tooling/README.md now marks each finding [merged: <file>] or [flagged] for provenance. - CORPUS_ollama_variants.md: annotate gemma4:26b as MoE (25.2B total / 3.8B active, 8-of-128 experts + 1 shared). Note Q4_K_M inference is standard (the "MoE quality degrades at 4-bit" caveat is training-only). Add note that audio on E-series is NOT available via Ollama — llama.cpp mmproj or vLLM only. - CORPUS_capabilities.md: native system role, configurable thinking mode, first trained tool use (vs Gemma 1/2/3 proof-of-concept), native object detection with bbox output in 1000x1000 coords, pointer to EmbeddingGemma for retrieval (Gemma 4 has no embedding mode). - CORPUS_tool_calling_format.md: add Chat Template Context section documenting the <|turn>/<turn|> asymmetric brackets (new in Gemma 4, replaced <start_of_turn>/<end_of_turn>) plus <|think>, <|channel>, <|image>, <|audio> tokens. Add HF transformers Alternative section showing processor.parse_response with response_schema. - GOTCHAS.md: add MEDIUM gotcha for abandoned google/gemma_pytorch (no Gemma 4 support since 2025-05-30). Expand fine-tuning section with FA2/FA4 head_dim=512 break, fused LoRA kernel issues, 26B A4B training-quant guidance, new tool-call tokens as learned embeddings. - SYNTHESIS.md: add banner pointing to tooling/ for canonical upstream material. Add embeddinggemma row to Model Selection table. Also: - Add .gitignore excluding .backup/ (local scratch per global CLAUDE.md convention, not needed in tracked history) and __pycache__/. - Add .claude/handoffs/2026-04-18-canonical-tooling-research.md so future sessions can pick up cold — facts verified, open threads, what changed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Gemma 4 is an ultra-compliant, highly-capable model that doesn't know who it is. It doesn't need hand-holding on tasks but needs explicit instructions in the system prompt about identity, boundaries, and output format. It needs `num_predict` increased (Ollama defaults are absurdly low), `think` set to false (thinking eats the context budget), and `format: json` avoided entirely (causes infinite loops). Due to its fast speed and free local inference, sequential tool calls are the ideal solution to tasks that would otherwise require long structured output.
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> **For canonical upstream source (model cards, chat templates, serving commands,
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> fine-tuning recipes, specialized siblings like EmbeddingGemma/ShieldGemma): see
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> `tooling/README.md`.** That directory is 147 files / 14 MB of first-party material
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> pulled from Google / Hugging Face / framework maintainers. This SYNTHESIS is the
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> opinionated digest; `tooling/` is the receipts.
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## Mental Model
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Think of Gemma 4 as a very competent employee on their first day. They can do the work — you don't need to explain how. But you DO need to explain:
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@@ -165,10 +171,11 @@ Vision is on ALL Gemma 4 variants (E2B, E4B, 26B, 31B). Audio is E-series only.
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| Use Case | Recommended | Why |
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|----------|------------|-----|
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| Production pipeline (needs GPU coexistence) | `gemma4:26b` | Best quality/speed/VRAM balance |
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| On-device / edge | `gemma4:e4b-it-q8_0` | 12GB VRAM, vision+audio |
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| Maximum quality (single-model GPU) | `gemma4:31b-it-q4_K_M` | Sharpest but slow under memory pressure |
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| Production pipeline (needs GPU coexistence) | `gemma4:26b` | MoE (3.8B active), fast, good quality/VRAM balance |
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| On-device / edge | `gemma4:e4b-it-q8_0` | 12GB VRAM, vision+audio (audio via llama.cpp only) |
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| Maximum quality (single-model GPU) | `gemma4:31b-it-q4_K_M` | Dense 31B, sharpest but 5x slower, more VRAM pressure |
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| Rapid prototyping / testing | `gemma4:26b` | Fast enough for interactive dev |
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| Retrieval / embeddings | `embeddinggemma` (308M, separate model) | Gemma 4 has no embedding mode; use the sibling |
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## Anti-Patterns
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