Mortdecai
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Architecture specs, benchmarks, gotchas, Ollama settings, tool calling format, and implementation patterns from Simon and AI_Visualizer. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2.1 KiB
2.1 KiB
Gemma 4 Capabilities Reference
Modalities
Text (all variants)
- Standard instruction-following, chat, completion
- System prompt support (critical — see synthesis)
- 128K context window (training length)
- 262K vocabulary
Vision (all variants)
- Tested and verified working (Seth, 2026-04-10)
- Accurately described colors, shapes, composition in 256x256 test image
- ~25 tok/s, ~24s end-to-end on pve197 V100
- Input: base64-encoded image in
imagesfield of Ollama API - Vision encoder: 16x16 patches, 2D RoPE, variable aspect ratio
- Token budgets scale with resolution (70-1120 soft tokens)
- Used in AI_Visualizer for SDXL frame quality criticism
Audio (E2B/E4B only)
- Not tested by Seth — status unknown in practice
- Conformer architecture (~300M params), mel-spectrogram input
- Trained on SPEECH ONLY — not music or environmental sounds
- Maximum 30 seconds per clip
- NOT available on 26B or 31B variants
- AI_Visualizer explicitly rejected audio for music analysis (DECISIONS S2) — correct call, model wasn't trained for it
Video (all variants)
- E2B/E4B: video WITH audio (
load_audio_from_video=True) - 31B/26B: video WITHOUT audio
- Not explicitly post-trained on video but works
- Maximum 60 seconds at 1 frame/second
- Not tested by Seth
Tool Calling / Function Calling
- Verified reliable in both Simon and AI_Visualizer
- Ollama native tool format (OpenAI-compatible function calling)
- Simon: 6 genealogy tools, up to 12 sequential iterations
- Supports parallel tool calls in single response
- Weak at deeply nested JSON schemas -> prefer sequential calls
Benchmark Context (vs Gemma 3)
- 31B replaces Gemma 3 27B (60 layers vs 62, but wider)
- MoE variant (26B) is new — no Gemma 3 equivalent
- E-series with PLE is new — on-device focus
- Proportional RoPE replaces linear frequency scaling -> better long-context
- Shared KV cache is new -> more efficient inference
What Gemma 4 Does NOT Do
- No native code execution / sandboxing
- No web browsing or retrieval
- Audio only on E-series (not the models most people run)
- No built-in RAG — tool calling can implement it