rafw007/ gemma4-26b-claude-coder:latest

47 yesterday

Niestandardowy model Gemma 4 26B (~25,8B parametrów), dostrojony do działania jako niezależny agent kodowania i administracji . Obsługuje API zgodne z Anthropic, dzięki czemu obsługuje Claude Code, Codex i Opencode

tools thinking 
ollama run rafw007/gemma4-26b-claude-coder

Details

yesterday

2cc446f3396b · 21GB ·

model
gemma4
·
25.2B
·
Q5_K_M
system
/nothink TRYB BEZ MYSLENIA: NIE generuj blokow rozumowania ani analizy. Zero <think>, zero kanalu an
params
{ "num_ctx": 65536, "repeat_penalty": 1.05, "temperature": 0.2, "top_k": 20, "to
template
{{ .Prompt }}

Readme

Gemma 4 26B coding agent for Claude Code / Codex / opencode, 64K+ context, native tool-calling, Q5_K_M GGUF, runs fully local on 32GB Apple Silicon.

Gemma 4 26B Claude Coder — local coding agent

A custom model built on Gemma 4 26B ( ~25.8B params), tuned to act as an autonomous coding and administration agent. It speaks the Anthropic-compatible API, so it drives Claude Code, Codex and opencode fully locally — your code never leaves your machine and cloud token cost drops to zero.

This is the 25 GB-class big sibling of the Gemma 4 Claude Coder family (E2B / E4B). It ships on a Q5_K_M GGUF quantization, deliberately chosen over Q4_K_M: the smaller Q4_K_M build injected token corruption into long code generations (broken tags, glued digit-letter tokens), and Q5_K_M fixes it — long files come out clean. The system prompt focuses on real work inside a codebase: use tools instead of guessing, write files instead of pasting, ground every answer in real tool output (never fabricate results), stay in one language, and always finish the file you start. No-think mode is wired into the system prompt for fast, direct answers.

Models in the family

Model Base Context Purpose
gemma4-26b-claude-coder Gemma 4 26B (~25.8B, Q5_K_M) 64K (native 256K) Strongest member — heavier reasoning and clean long-code generation on 32 GB hardware.
gemma4-e4b-claude-coder Gemma 4 E4B (eff. 4B / 8B w/ embeddings) 64K Stronger 16 GB coder — reasoning and tool use on larger tasks.
gemma4-e2b-claude-coder Gemma 4 E2B (eff. 2B / 5.1B w/ embeddings) 64K Fast everyday 16 GB coder — edits, autocomplete, short agent loops.

What it’s for

  • Driving Claude Code / Codex / opencode locally (ollama launch claude --model rafw007/gemma4-26b-claude-coder).
  • Agentic code writing and editing with native function calling / tool use.
  • Administration and devops tasks on a server — real nmap, df, du with no hallucinated output.
  • Full privacy and offline operation — no code sent to the cloud.

Measured behavior (June 2026 tests)

  • Tool-calling without hallucination — real message.tool_calls, and admin tasks (df/du, full /24 nmap scans with host tables) report the actual output rather than inventing it.
  • Clean long code (the headline fix) — a full pygame Tetris generated complete, runnable and syntactically valid (200+ lines), with zero corruption signatures and zero language drift on a task that broke under the Q4_K_M build.
  • Guardrails intact — this is the non-abliterated base, so it refuses to write malware.
  • No-think holds on the direct path — empty thinking field, content is clean.

Context

  • 64K tokens configured — matching Claude Code’s recommendation (64K minimum).
  • Base Gemma 4 26B natively supports 256K, and on 32 GB Apple Silicon the engine serves the full native window 100% on GPU (sliding-window attention keeps the KV cache small), so context never becomes the bottleneck.

Test hardware

The model was built and tested on:

  • Mac Studio M2, 32 GB-class — Ollama 0.30, GPU (Metal) inference
  • Mac Mini M4, 32 GB RAM, macOS — Ollama 0.30, GPU (Metal) inference (32 GB-class target)
  • Quantization: Q5_K_M (~21 GB) and Q6_K (~23 GB) GGUF builds available

Measured performance

Placement Hardware Speed Tool calling
100% GPU, native ctx, CONTEXT 65536 Mac Studio M2 ~52-56 tok/s native, real message.tool_calls

The model loads entirely on the GPU with no CPU spill (verified via ollama ps: 100% GPU, CONTEXT 65536). The only real cost is a one-time cold load of the ~21 GB weights, not a per-turn cost; warm generation runs ~52-56 tok/s on the Studio. The Mac Mini M4 (32 GB) is the same 32 GB target class — bounded by memory bandwidth rather than the model.

No-think mode

The whole Gemma 4 family has thinking baked into the weights. The system prompt ships with /nothink + an anti-reasoning instruction, which works on the direct API path and under opencode/codex. Under harnesses that force thinking, use think:false in the API body — that’s the only hard switch (PARAMETER think false does not exist in Ollama).

Note on long code generation

Q5_K_M removes the bulk corruption seen on the smaller Q4_K_M build — in testing, long single-pass generations came out clean (zero .->- glitches, zero language drift). If you generate files for production, a quick corruption scan before use is still good practice, but the Q5_K_M build tested clean on the long-code task that previously failed.

How it was made

Designed, built and tested with the help of Claude Opus 4.8 — the best coding model in the world. Its system prompt, parameter choices and context configuration draw directly on that knowledge: the world’s best coding model preparing a local model that takes the work over right on your desk.

Available files

File Quant Size Notes
gemma4-26b-claude-coder-Q5_K_M.gguf Q5_K_M ~21 GB Recommended balance of quality/size; fits 32 GB with full 64K ctx.

Both are derived from the same google/gemma-4-26B-A4B-it base and carry the identical Claude Coder system prompt and parameters (see Modelfile).

License

Apache 2.0 (inherited from the base Gemma 4).