# 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 |
| `CORPUS_tool_calling_2026-05.md` | **May 2026 update — read first for tool-call debugging.** Ollama parser fixes (#15539), `think` flag properly characterized, the "doesn't fire tools without explicit ask" research (Probe & Prefill, BiasBusters, When2Call), XGrammar-2. Supersedes parts of `GOTCHAS.md` (think:false rule, Vulkan loop, streaming bug all updated in-place with pointers here) | When debugging tool-call reliability, deciding `think` value, or planning a router/probe layer |
| `CORPUS_agentic_retrieval.md` | Post-RAG landscape — hybrid retrieval + cross-encoder rerank as the settled base, CRAG-shaped flows in LangGraph/LlamaIndex, GraphRAG (LightRAG/LazyGraphRAG), memory architectures (mem0 vs Letta MemFS), deep-research agents (gpt-researcher, smolagents), production-vs-experimental sorting | When stepping up from basic embedding RAG to agentic retrieval, picking a memory layer, or scoping a deep-research agent |
| `CORPUS_cli_coding_agent.md` | Positioning Gemma 4 for CLI coding agent use (openclaw / open code / pi / hermes / aider style). Honest take on what Google did and didn't measure, head-to-head with `qwen3-coder:30b`, homelab setup pointer | When scoping a CLI coding agent or deciding Gemma 4 vs Qwen3-Coder |
| `docs/openwebui-setup.md` | How to configure Gemma 4 inside OpenWebUI — per-setting reference, two ready-to-bake Workspace Model profiles (chat + extract), and a symptom→cause troubleshooting table mapped back to GOTCHAS.md. Assumes Ollama + OpenWebUI are already running. | When setting up or debugging a Gemma 4 model in OpenWebUI, or handing the front-end config to someone else |
| `docs/reference/bakeoff-2026-04-18.md` | CLI-coding-agent bakeoff on 3090 Ti. **Rounds 1/2 misidentified the cause; Round 3 (the correct one): `think: false` silent-stops gemma4:26b at certain multi-turn states on 32K context.** 31B and Qwen3-Coder robust to the flag. Harness at `scripts/bakeoff/` | When deciding which model to back a CLI agent with, writing a custom agent payload, or debugging a silent tool-call halt |
| `docs/reference/mort-bakeoff-2026-04-18.md` | mort-bot-specific `think=true` vs `think=false` bakeoff on mort's actual loop shape (gemma4:26b, num_ctx=8192). **Thinking does NOT accumulate in context on Ollama 0.20.4** — strips it from serialized history. Both settings behave identically on step counts, tool counts, wall clock. Harness at `scripts/mort-bakeoff/` | When deciding mort-bot's THINK env var, or when someone claims "think=true eats context" without pinning an Ollama version |
| `docs/reference/gpu-bakeoff-2026-04-20.md` | Cross-GPU throughput bakeoff: steel141 RTX 3090 Ti vs strix-halo (AMD Strix Halo). **3090 Ti wins decode decisively (128 tok/s on 26B MoE). Strix gets ~42% of that on ~25% of the bandwidth.** Also quantifies the MoE vs dense gap: 26B decodes ~4.7× faster than 31B on both cards. Harness at `scripts/gpu-bakeoff/` | When choosing which host to run a Gemma 4 workload on |
| `tooling/` | **Canonical upstream tooling** — real scripts, notebooks, model cards, and configs pulled from Google / HF / framework maintainers (147 files). Subdirs: `google-official/`, `huggingface/`, `inference-frameworks/`, `gemma-family/`, `fine-tuning/`. See `tooling/README.md` for index and findings that update the older `CORPUS_*` docs | When you need authoritative source material — model cards, chat templates, fine-tuning recipes, serving commands for vLLM / llama.cpp / MLX, or to scope a specialized sibling (ShieldGemma, EmbeddingGemma, etc.) |

## 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.
