Instructions to use CodeMonkey1/gemma-superpowers-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeMonkey1/gemma-superpowers-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CodeMonkey1/gemma-superpowers-gguf",
	filename="gemma-superpowers-v2.7.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CodeMonkey1/gemma-superpowers-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Use Docker

docker model run hf.co/CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use CodeMonkey1/gemma-superpowers-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeMonkey1/gemma-superpowers-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeMonkey1/gemma-superpowers-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Ollama
How to use CodeMonkey1/gemma-superpowers-gguf with Ollama:
```
ollama run hf.co/CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
```

Unsloth Studio

How to use CodeMonkey1/gemma-superpowers-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CodeMonkey1/gemma-superpowers-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CodeMonkey1/gemma-superpowers-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CodeMonkey1/gemma-superpowers-gguf to start chatting

How to use CodeMonkey1/gemma-superpowers-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CodeMonkey1/gemma-superpowers-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CodeMonkey1/gemma-superpowers-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use CodeMonkey1/gemma-superpowers-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "CodeMonkey1/gemma-superpowers-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use CodeMonkey1/gemma-superpowers-gguf with Docker Model Runner:
```
docker model run hf.co/CodeMonkey1/gemma-superpowers-gguf:Q4_K_M
```

Lemonade

How to use CodeMonkey1/gemma-superpowers-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CodeMonkey1/gemma-superpowers-gguf:Q4_K_M

Run and chat with the model

lemonade run user.gemma-superpowers-gguf-Q4_K_M

List all available models

lemonade list

🦾 Gemma-4-12B — Superpowers Edition (v2.7)

A local coding companion that thinks before it types, calls tools natively, looks things up instead of guessing, and — given a real agent runtime — actually does the work. Now it holds its discipline deep into a long context.

Most models hear "build me a tool" and immediately vomit code. Not this one. Gemma-4-12B-Superpowers has six engineering disciplines fine-tuned into its weights — so it reaches for the right method on its own, with no system prompt, no jailbreak, no babysitting.

⚡ What she does differently — automatically

You say…	She does…
"Build me an X"	🧠 Brainstorms — asks the right questions, weighs 2–3 approaches before a line of code
"It's broken / wrong output"	🔬 Root-causes — reproduces & isolates before patching (no guess-fixing)
"Implement this feature"	✅ Test-first — failing test → watch it fail → minimal code
"Here's a multi-step task"	🗺️ Plans — an ordered roadmap before touching files
"Is it done? Ship it."	🔎 Verifies — runs the check, shows the evidence (no "should work")
"Write a runbook for X"	📓 Documents — clean SOPs with triggers + red flags

…and she won't over-think a one-liner — ask "what's 2+2" and you get 4, not a discovery meeting.

📏 Holds the line deep in context — new in v2.7

Earlier versions could drift once the window filled up — big file loads and long agent sessions diluted the disciplines. v2.7 was retrained at long sequence length (max_seq_len=18432) on real long-context trajectories — deep gather→synthesize→stop sessions, big memory loads, large-file edits, and early-fact-retention (a constraint stated early and correctly honored late). Result: she stays disciplined, keeps calling tools cleanly, and still recalls facts stated tens of thousands of tokens earlier — validated with a dedicated long-context gate (synthesis + early-fact recall + no salad/loop at ~12k). Practical sweet spot is ≤16k; give her the room (see Quick start).

🔧 Native tool-calling — validated in agent mode

She emits Gemma-4's native <|tool_call> format and, in a real agent runtime, drives a clean multi-step loop. Verified end-to-end (Jan, agent mode): on "check your memory and let's start," she ran list_directory → read_multiple_files → noticed a wrong path and adapted → asked a scoping question → followed the project's handoff protocol → synthesized and got to work. Real calls, real results, no re-read loop, no confabulation. Trained against real tool schemas (filesystem, WordPress/MCP, skills) and many-tools-at-once menus, so she picks the right tool from a big menu and emits correct argument names.

🏃 Run her in an agent runtime. For tool work, use a host that executes tools server-side and bounds the agent loop — Jan (recommended) or AnythingLLM agent mode. A bare chat UI that only does single-turn completion won't dispatch her calls. Keep repeat_penalty ≈ 1.0–1.1 and penalties otherwise off — high penalties shred tool calls.

🔎 Looks it up instead of guessing

Meets an unfamiliar tool or library? She reaches for docs — context7 (resolve-library-id → query-docs) for libraries, web search/visit otherwise — then makes the correct call. New MCP tool added later works without a retrain: she discovers how to use it rather than fabricating arguments.

🛠️ Built for real work — WordPress, head to toe

~40% of her training lives in the trenches: PHP (ACF, hooks, WP_Query, WP-CLI), JavaScript (Gutenberg, enqueued scripts, jQuery/vanilla), and CSS (responsive, CLS-safe fonts, WCAG 2.2). She debugs your stack, not toy code.

🚀 Quick start

Files are version-stamped so you always know what you're loading:

gemma-superpowers-v2.7.Q4_K_M.gguf — fast daily driver (~7.4 GB). Fully offloads on a 12 GB GPU.
gemma-superpowers-v2.7.Q6_K.gguf — maximum tool-selection precision (~9.8 GB).

Load in Jan (or LM Studio). On a 12 GB card: full GPU offload, q8_0 KV cache + flash attention, context ~16k (the range v2.7 was trained to hold), and enable Context Shift so long agent sessions never overflow-crash. More VRAM? Give her more — she'll use it.
Leave the discipline system prompt EMPTY — the behavior is baked in. (A one-line Memory hub: <path> is fine if you use a filesystem memory convention.)
For tools: use agent mode and a sane repeat_penalty (≈1.0–1.1).

Vision: grab mmproj-gemma-4-12B-it-BF16.gguf (stock Gemma-4 projector) into the same folder. Vision weights are untouched by the text fine-tune. Note: feed her images, not giant pasted screenshots — a full-resolution screenshot can balloon into ~100k+ tokens and blow the window.

🔧 Under the hood

QLoRA on unsloth/gemma-4-12b-it via Unsloth: r=32, lora_alpha=64, 3 epochs, max_seq_len=18432 (v2.7 long-context; earlier versions 4096), canonical Gemma-4 chat template, no discipline system prompt in training (disciplines unconditioned). Data = discipline examples + "answer-it-straight" negatives + native tool/agentic chains + lookup trajectories + agentic gather→synthesize→stop + long-context trajectories (deep sessions, big loads, large-file edits, early-fact retention) + general/Dolly blend. Methodology adapted from the open-source superpowers skills (MIT).

🧬 Version history

v2.7 (2026-07-02) — long-context. Retrained at max_seq_len=18432 on real long-context trajectories so the disciplines, tool-calling, and early-fact recall hold deep into the window instead of drifting once it fills. Dedicated long-context gate passes (synthesis + early-fact recall + no salad/loop at ~12k) with no regression on the short-context gates (native tool-call, lookup, autoloop, memory-load). Practical sweet spot ≤16k.
v2.6 (2026-06-18) — agentic, validated. Trained the full operating discipline (memory-load habit, gather→stop, /handoff, style); confirmed working end-to-end in Jan agent mode — real multi-step tool use, scoping questions, follows handoff protocol, no loop/confabulation. Lesson learned the hard way: the residual "loops" in earlier testing were a serving-layer issue (a single-engine chat UI mis-parsing/over-driving the tool loop), not the weights — run her in an agent runtime and she performs. Version-stamped filenames ship from here on.
v2.4 (2026-06-17) — stop-discipline. Single-call→answer trajectories; held-out loop gate.
v2.2 (2026-06-17) — lookup generalization. Looks up unfamiliar tools/libraries; recovers from tool errors.
v2.1 (2026-06-16) — native tool-calling. Canonical template, native <|tool_call>.
v1.1 / v1 — serving fixes / experimental.

⚠️ Status

v2.7. Disciplines fire with no system prompt; native tool-calling + lookup + agentic gather→stop validated in agent mode, now with long-context retention validated to ~12k and practical to ~16k. It's a 12B running locally — strong and consistent in a real agent runtime, not frontier-perfect. Inherits the Gemma Terms of Use.

Fine-tuned with stubbornness and a few dollars of GPU time. 🧪

Downloads last month: 1,717

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

6-bit

Model tree for CodeMonkey1/gemma-superpowers-gguf

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

unsloth/gemma-4-12b-it

Quantized

(3)

this model