Mortdecai
5011059f5d
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>
1.6 KiB
1.6 KiB
gemma4-research
Research corpus and implementation guidance for Google Gemma 4, based on production use in Seth's homelab.
Files
| File | What | When to Read |
|---|---|---|
SYNTHESIS.md |
Start here. Opinionated guide — how to build with Gemma 4 | Before any new Gemma 4 implementation |
GOTCHAS.md |
Known issues and workarounds, severity-ranked | When debugging Gemma 4 issues or starting a new project |
IMPLEMENTATIONS.md |
Patterns from Simon and AI_Visualizer | When designing a new Gemma 4 integration |
CORPUS_architecture.md |
Model architecture details (layers, attention, PLE, MoE) | When you need to understand WHY Gemma 4 behaves a certain way |
CORPUS_ollama_variants.md |
Available models, sizes, VRAM, Ollama settings | When choosing a model variant or configuring Ollama |
CORPUS_capabilities.md |
Modalities (vision, audio, video, tools), what it can/can't do | When scoping what Gemma 4 can handle |
CORPUS_benchmarks.md |
Full benchmark table vs Gemma 3, arena scores, agentic scores | When comparing Gemma 4 to alternatives |
CORPUS_tool_calling_format.md |
Native token format + JSON API format for function calling | When implementing tool calling |
Source Projects
- Simon (
~/bin/FreibergFamily/simon/) — Multi-turn chat agent with 6 tools, genealogy historian - AI Visualizer (
~/bin/AI_Visualizer/) — Music video generator, 4-stage Gemma pipeline + vision
Key Insight
Gemma 4 is ultra-compliant and highly capable but doesn't know who it is. It needs explicit system prompts, not hand-holding. Due to fast local inference, sequential tool calls beat long JSON requests.