Mortdecai
5775978899
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>
65 lines
3.3 KiB
Markdown
65 lines
3.3 KiB
Markdown
# Gemma 4 Capabilities Reference
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## Modalities
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### Text (all variants)
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- Standard instruction-following, chat, completion
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- **Native `system` role support** (new in Gemma 4; Gemma 3 prepended system as user turn)
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- **Configurable thinking mode** — `<|think|>` / `<|channel>` tokens in the chat template; Ollama `think: true/false` flag. Seth's finding (see GOTCHAS): keep `false` for tool-use workloads.
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- 128K context window (E2B/E4B) / 256K (26B/31B) — training length
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- 262K vocabulary
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### Vision (all variants)
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- **Tested and verified working** (Seth, 2026-04-10)
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- Accurately described colors, shapes, composition in 256x256 test image
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- ~25 tok/s, ~24s end-to-end on pve197 V100
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- Input: base64-encoded image in `images` field of Ollama API
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- Vision encoder: 16x16 patches, 2D RoPE, variable aspect ratio
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- Token budgets scale with resolution (70-1120 soft tokens)
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- Used in AI_Visualizer for SDXL frame quality criticism
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### Audio (E2B/E4B only)
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- **Not tested by Seth** — status unknown in practice
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- Conformer architecture (~300M params), mel-spectrogram input
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- **Trained on SPEECH ONLY — not music or environmental sounds**
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- Maximum 30 seconds per clip
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- NOT available on 26B or 31B variants
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- AI_Visualizer explicitly rejected audio for music analysis (DECISIONS S2) — correct call, model wasn't trained for it
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### Video (all variants)
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- E2B/E4B: video WITH audio (`load_audio_from_video=True`)
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- 31B/26B: video WITHOUT audio
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- Not explicitly post-trained on video but works
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- Maximum 60 seconds at 1 frame/second
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- Not tested by Seth
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### Tool Calling / Function Calling
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- **Verified reliable** in both Simon and AI_Visualizer
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- Ollama native tool format (OpenAI-compatible function calling)
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- Simon: 6 genealogy tools, up to 12 sequential iterations
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- Supports parallel tool calls in single response
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- Weak at deeply nested JSON schemas -> prefer sequential calls
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- **First Gemma generation with tool use as a trained capability.** Gemma 1/2/3 tool use was "proof-of-concept" (per the DeepMind tool_use colab). Gemma 4 has dedicated tool-call tokens and is trained on the pattern.
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### Native Object Detection (all variants)
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- **Prompt format:** "Detect the {object} in this image" → structured output `{box_2d: [ymin, xmin, ymax, xmax]}` in **1000×1000-normalized coordinates** (rescale to your actual image dims).
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- Images auto-resized to multiples of 48 pixels by the processor.
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- Useful for grounding, cropping, counting, or passing bboxes to downstream tools — no separate detection model required.
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- Documented in the HF model card (`tooling/huggingface/model-cards/gemma-4-*.md`). Not tested by Seth yet.
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## Benchmark Context (vs Gemma 3)
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- 31B replaces Gemma 3 27B (60 layers vs 62, but wider)
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- MoE variant (26B) is new — no Gemma 3 equivalent
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- E-series with PLE is new — on-device focus
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- Proportional RoPE replaces linear frequency scaling -> better long-context
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- Shared KV cache is new -> more efficient inference
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## What Gemma 4 Does NOT Do
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- No native code execution / sandboxing
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- No web browsing or retrieval
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- Audio only on E-series (not the models most people run) — and **not on Ollama**, requires llama.cpp mmproj or vLLM
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- No built-in RAG — tool calling can implement it
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- No embeddings — use `EmbeddingGemma` (308M, separate model) for retrieval/semantic search
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