Instructions to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF 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 elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
# Run inference directly in the terminal:
llama cli -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
# Run inference directly in the terminal:
llama cli -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

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 elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
# Run inference directly in the terminal:
./llama-cli -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

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 elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

Use Docker

docker model run hf.co/elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

LM Studio
Jan
Ollama
How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with Ollama:
```
ollama run hf.co/elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
```
Unsloth Desktop

How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with Docker Model Runner:
```
docker model run hf.co/elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF
```

Lemonade

How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

Run and chat with the model

lemonade run user.nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

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 elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF

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 "elpirater312/nohurry-gemma-4-26B-A4B-it-heretic-IQ4_NL-ud-imtrx-GUFF" \
  --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"

⚠️ UPDATE APRIL 09: V2 RELEASE (CRITICAL FIXES)

The previous version was built on outdated llama.cpp kernels. This V2 release incorporates the latest architectural fixes for Gemma-4, including the <unused24> buffer fix and Logit Soft-Capping.

THE ULTIMATE IQ4_NL (V2) - APRIL 2026 FIXES

The previous quant is out of date and MISSING critical architectural fixes. This V2 release is the first to incorporate the Unsloth Dynamic 2.0 re-computed imatrix and the latest llama.cpp Gemma-4 patches.

What's New in V2? (The Unsloth Special)

CRITICAL FIX: <unused24> token buffer overlap fixed (No more random logic crashes).
KV-Cache Support: Full attention rotation for heterogeneous iSWA (Better long-context stability).
Vocab Fix: Byte token handling added to BPE de-tokenizer (Fixes weird character output).
BOS Logic: add_bos now defaults to True for native Gemma-4 behavior.
Logit Soft-Capping: Correctly reads final_logit_softcapping (CRITICAL for preventing NaN/Infinity loops).
Newline Logic: Custom newline split logic for superior formatting.

Why this Quant?

Format: IQ4_NL (Non-Linear) - Higher logic-per-bit than Q4_K_M or Q4_0.
Brain: High-res Q8_0 Token Embeddings and Output Head (Prevents vocabulary degradation).
Speed: Specially optimized for CPU/Laptop P-Cores (~7 t/s on 12th Gen i3 Dual DDR4 Channel at 3200MHz, CPU ONLY inference!).
Matrix: Built with the latest April 07 re-computed Unsloth imatrix.

All credits to Merijn Hendriks (nohurry) for the heretic weights and Unsloth for the imatrix file.