Instructions to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-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 EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

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 EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

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 EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Use Docker

docker model run hf.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

LM Studio
Jan

vLLM

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-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": "EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Ollama
How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with Ollama:
```
ollama run hf.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
```
Unsloth Desktop

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

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": "EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF
```

Lemonade

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-heretic-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-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 EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

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 EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF

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 "EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF" \
  --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"

EmanuelOverride commited on Jul 11

Commit

2eed316

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1 Parent(s): fda0f63

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@@ -34,12 +34,12 @@ This repository contains custom **APEX (Adaptive Precision for EXpert Models)**
 ### 🚀 The 26B Custom Intermediary Suite
 In my initial attempts at creating an intermediary size-tier between Compact and Balanced, I scaled up the basic logic of the Compact configuration by one quant level:
-* **[i-Upscale.Premium](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/blob/main/gemma-4-26B-A4B-it-heretic.APEX.i-Upscale.Premium.gguf?download=true):** `14,600.94 MiB` (4.85 BPW) — *Performance King on legacy hardware.* Employs a `[Q5_K -> Q4_K -> Q3_K]` stepped gradient across the mid-layer experts while shielding context gates.
-* **[i-Upscale.Balanced](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/blob/main/gemma-4-26B-A4B-it-heretic.APEX.i.Upscale.Balanced.gguf?download=true):** `14,790.00 MiB` (4.92 BPW) — Flat `Q4_K` expert mapping across all up/down tensors. Known and stable size-to-precision efficiency curve.
-* **[i-Upscale.Econ](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/blob/main/gemma-4-26B-A4B-it-heretic.APEX.i.Upscale.Econ.gguf?download=true):** `12,899.38 MiB` (4.29 BPW) — Aggressive `Q4_K` to `Q3_K` mid-expert trimming. *Note for Legacy Hardware:* Seems to unfurl into a wider runtime cache overhead on older CPUs; maybe better on high-throughput modern AVX-512 or AMX architectures?
 ### 🎛️Experimental Addition: Harmonic Resonance v2
-* **[i-Harmonic.Resonance.v2](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/blob/main/gemma-4-26B-A4B-it-heretic.APEX.i.HarmonicResonance.gguf?download=true):** `15,582.23 MiB` (5.18 BPW)
 An advanced piece of custom neural topology synchronized to the model's native **6-layer global attention pulse relays (Layers 5, 11, 17, 23, 29)**.
 By eliminating the prime-number noise of 5-bit layers and shifting non-divisible legacy fallback rows with calibrated Importance Quantization (`IQ4_NL`), the data wave moves in a continuous binary octave sequence (`Q8_0 -> Q6_K -> Q4_K -> Q3_K`). From testing: Elevated lateral thinking, crystalline prose focus, and higher intentionality during long-context interactions, but at the cost of some performance and memory overhead.

 ### 🚀 The 26B Custom Intermediary Suite
 In my initial attempts at creating an intermediary size-tier between Compact and Balanced, I scaled up the basic logic of the Compact configuration by one quant level:
+* **[i-Upscale.Premium](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/resolve/main/gemma-4-26B-A4B-it-heretic.APEX.i-Upscale.Premium.gguf):** `14,600.94 MiB` (4.85 BPW) — *Performance King on legacy hardware.* Employs a `[Q5_K -> Q4_K -> Q3_K]` stepped gradient across the mid-layer experts while shielding context gates.
+* **[i-Upscale.Balanced](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/resolve/main/gemma-4-26B-A4B-it-heretic.APEX.i.Upscale.Balanced.gguf):** `14,790.00 MiB` (4.92 BPW) — Flat `Q4_K` expert mapping across all up/down tensors. Known and stable size-to-precision efficiency curve.
+* **[i-Upscale.Econ](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/resolve/main/gemma-4-26B-A4B-it-heretic.APEX.i.Upscale.Econ.gguf):** `12,899.38 MiB` (4.29 BPW) — Aggressive `Q4_K` to `Q3_K` mid-expert trimming. *Note for Legacy Hardware:* Seems to unfurl into a wider runtime cache overhead on older CPUs; maybe better on high-throughput modern AVX-512 or AMX architectures?
 ### 🎛️Experimental Addition: Harmonic Resonance v2
+* **[i-Harmonic.Resonance.v2](https://huggingface.co/EmanuelOverride/gemma-4-26B-A4B-it-heretic-APEX-GGUF/resolve/main/gemma-4-26B-A4B-it-heretic.APEX.i.HarmonicResonance.gguf):** `15,582.23 MiB` (5.18 BPW)
 An advanced piece of custom neural topology synchronized to the model's native **6-layer global attention pulse relays (Layers 5, 11, 17, 23, 29)**.
 By eliminating the prime-number noise of 5-bit layers and shifting non-divisible legacy fallback rows with calibrated Importance Quantization (`IQ4_NL`), the data wave moves in a continuous binary octave sequence (`Q8_0 -> Q6_K -> Q4_K -> Q3_K`). From testing: Elevated lateral thinking, crystalline prose focus, and higher intentionality during long-context interactions, but at the cost of some performance and memory overhead.