Mortdecai
a945207aab
Ran minimal agent loop (Ollama /api/chat + read_file/write_file/run_bash) on steel141 3090 Ti against 3 models on a broken-median-function task: - gemma4:31b-it-q4_K_M: PASS (8 iters, 1 write, 44s) — textbook trace - qwen3-coder:30b: PASS (15 iters, 1 write, 22s) — correct but chatty - gemma4:26b: FAIL (6 iters, 0 writes) — silently stops with eval=4 after reading source. Reproduced on second run. One-shot probe confirms 26b CAN produce the correct fix — failure is specifically at the write_file tool-call argument boundary. Updates GOTCHAS with a new HIGH-severity entry, SYNTHESIS model-selection table, CORPUS_cli_coding_agent.md empirical-follow-up pointer, and adds docs/reference/bakeoff-2026-04-18.md with the full writeup.
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/reference/bakeoff-2026-04-18.md |
Raw results: CLI-coding-agent bakeoff of gemma4:26b / gemma4:31b / qwen3-coder:30b on steel141 3090 Ti. 31B clean, Qwen3-Coder correct but chatty, 26B reproducibly silent-stops at write_file. Harness at scripts/bakeoff/ |
When deciding which model to back a CLI agent with, or debugging a similar tool-call halt |
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.