Files
gemma4-research/README.md
T
Mortdecai 22af59756f docs: remove V100 from GPU bakeoff
V100 data was degraded by SDXL co-residence on CT 167 (31/32 GB VRAM
occupied, Gemma 4 models forced 95% onto CPU). Rather than ship a
prominent caveat, drop the V100 column entirely so the doc reports
only apples-to-apples measurements. V100 can be added back once an
isolated run is possible.

Removed: V100 column from TL;DR and per-model tables, hardware row,
caveat section, and associated raw JSONs under runs/pve197/. Harness
config keeps pve197 in HOSTS for future re-runs.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-20 05:47:41 -04:00

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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
docs/reference/gpu-bakeoff-2026-04-20.md Cross-GPU throughput bakeoff: steel141 RTX 3090 Ti vs matt-strix (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.