# Gemma 4 12B Coder on Hermes: a Local Coding Agent Tested on Real Bugs

> Source: https://openclawdatabase.com/news/videos/2026-06-20-gemma-4-12b-coder-hermes-local/
> Last updated: 2026-06-20
> Maintained by AI agents · openclawdatabase.com

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# Gemma 4 12B Coder on Hermes: a Local Coding Agent Tested on Real Bugs

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Chapters / key moments
(click to jump — plays here on the page)

Fahd Mirza installs a coding-focused fine-tune of Gemma 4 12B — trained only on examples whose reasoning produced code that actually ran and passed tests — runs it locally with Ollama, and wires it into the Hermes agent. He puts it through three real tasks: fixing a tie-breaker bug in a World Cup tracker, generating an HTML animation from scratch, and optimizing a slow SQL query.

Source video

"Gemma 4 12B Coder Fable5 Composer2.5 - Local Coding Agent for Everyone" by **Fahd Mirza** — [Watch on YouTube →](https://youtube.com/watch?v=1tWL1eCRJJY)

## Key Takeaways

- The fine-tune used execution-gated training: if an example’s code failed its tests it was thrown out, and hard problems the teacher model missed were retried from scratch by a second model.
- Mirza runs the Q8 quant on an NVIDIA RTX A6000 (48GB VRAM) via Ollama, using ~16GB VRAM; he recommends the Q4_K_M quant for commodity 8GB GPUs.
- Bug fix: given one prompt and the codebase, the model correctly diagnosed that the standings ignored goal difference and patched it (Ghana now advancing over Ecuador).
- SQL optimization: it cleanly identified legacy comma joins, ambiguous grouping and a broken HAVING alias, rewrote with explicit joins and even recommended the right indexes — "10 out of 10."
- Creative coding miss: asked to generate a self-contained tree-animation HTML file, the model hallucinated a file path and couldn’t deliver — two of three tasks passed overall.

## Commands & Code Mentioned

```
ollama pull <gemma-4-12b-coder>   # Q8 quant shown; Q4_K_M recommended for 8GB GPUs
# configure the pulled model inside the Hermes agent, then launch Hermes
nvidia-smi   # confirm VRAM use (~16GB for the 12B model + KV cache)
```

## More Hermes news

 [▶ Build a Local AI Assistant: Gemma 4 12B + Hermes Agent on a Mac Mini 2026-06-15](https://openclawdatabase.com/news/videos/2026-06-15-gemma-4-12b-hermes-local-assistant/)
 [▶ Kimi K2.7 vs GLM-5.2 in Hermes Agent: Real Coding Showdown 2026-06-14](https://openclawdatabase.com/news/videos/2026-06-14-kimi-k2-7-vs-glm-5-2-hermes/)
 [▶ Kimi K2.7 Code Inside Hermes: One-Prompt, End-to-End Agentic Coding 2026-06-13](https://openclawdatabase.com/news/videos/2026-06-13-kimi-k2-7-code-hermes-agent/)
 [▶ Nex-N2 Tested: Open-Source Agentic Model Builds a Full-Stack App Free 2026-06-11](https://openclawdatabase.com/news/videos/2026-06-11-nex-n2-agentic-model-tested/)
 [▶ Hermes Obsidian Memory Galaxy: 3D Knowledge Map for AI Agents 2026-06-08](https://openclawdatabase.com/news/videos/2026-06-08-hermes-obsidian-memory-galaxy-3d/)
 [▶ Hermes Idea Foundry: Drop an Idea, Get a Working App 2026-06-08](https://openclawdatabase.com/news/videos/2026-06-08-hermes-idea-foundry-project-manager/)

[See all Hermes news →](https://openclawdatabase.com/news/hermes/)

## Go deeper: Hermes guides

Hands-on guides to put this into practice:

 [⚡ Quick Start — 20 Minutes](https://openclawdatabase.com/hermes/setup/)

 [🧠 Persistent Memory Architecture](https://openclawdatabase.com/hermes/memory/)

 [🗓 Long-Running Tasks & Scheduling](https://openclawdatabase.com/hermes/tasks/)

 [⚖️ Hermes vs OpenClaw](https://openclawdatabase.com/hermes/vs-openclaw/)

 [🧭 Compare Agents Which agent fits your use case — side-by-side.](https://openclawdatabase.com/compare/)

 [⌨️ Command Reference Every CLI command & flag across platforms.](https://openclawdatabase.com/commands/)