Mortdecai 7f4e956466 docs: session handoff — OpenWebUI setup doc
Chains from 2026-04-18-canonical-tooling-research. Captures the
settings-guide work shipped in d9477da and the repo-convention
note (push-on-commit for ~/bin Gitea projects).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 23:42:05 -04:00

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_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
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.

S
Description
Gemma 4 research corpus, gotchas, and implementation patterns for local inference
Readme 5.4 MiB
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Jupyter Notebook 79.5%
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Python 7.5%
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