Instructions to use AxomLabs/axom-x1-ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AxomLabs/axom-x1-ultra 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 AxomLabs/axom-x1-ultra:Q4_0
# Run inference directly in the terminal:
llama cli -hf AxomLabs/axom-x1-ultra:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AxomLabs/axom-x1-ultra:Q4_0
# Run inference directly in the terminal:
llama cli -hf AxomLabs/axom-x1-ultra:Q4_0

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 AxomLabs/axom-x1-ultra:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf AxomLabs/axom-x1-ultra:Q4_0

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 AxomLabs/axom-x1-ultra:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AxomLabs/axom-x1-ultra:Q4_0

Use Docker

docker model run hf.co/AxomLabs/axom-x1-ultra:Q4_0

LM Studio
Jan
Ollama
How to use AxomLabs/axom-x1-ultra with Ollama:
```
ollama run hf.co/AxomLabs/axom-x1-ultra:Q4_0
```
Unsloth Desktop

How to use AxomLabs/axom-x1-ultra with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AxomLabs/axom-x1-ultra:Q4_0

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": "AxomLabs/axom-x1-ultra:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AxomLabs/axom-x1-ultra with Docker Model Runner:
```
docker model run hf.co/AxomLabs/axom-x1-ultra:Q4_0
```

Lemonade

How to use AxomLabs/axom-x1-ultra with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AxomLabs/axom-x1-ultra:Q4_0

Run and chat with the model

lemonade run user.axom-x1-ultra-Q4_0

List all available models

lemonade list

Hermes Agent

How to use AxomLabs/axom-x1-ultra with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AxomLabs/axom-x1-ultra:Q4_0

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 AxomLabs/axom-x1-ultra:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AxomLabs/axom-x1-ultra with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AxomLabs/axom-x1-ultra:Q4_0

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 "AxomLabs/axom-x1-ultra:Q4_0" \
  --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"

Axom X1-Ultra — X1-Ultra (26B MoE Q4)

Files an Axom install downloads for the X1-Ultra tier (axom init), mirrored here so no Hugging Face gate stands between a user and the model. Gemma 4 is Apache 2.0.

file	what	sha256
`gemma-4-26B_q4_0-it.gguf`	chassis: the official Google QAT GGUF, unmodified	`3eca3b8f6d7baf218a7dd6bba5fb59a56ee25fe2d567b6f5f589b4f697eca51d`
`ops_26b_v1-lora-f16.gguf`	ops leaf (ops_26b_v1)	`54e0929e6fa77c2e8fb1b13257e4d00645731f9b73810d14f658862c82433fe1`

The chassis is the official google/gemma-4-26B-A4B-it-qat-q4_0-gguf file, byte for byte. The leaf is an attention-only LoRA pressed by Axom Labs on the QAT-unquantized base of the same file. Runtime and docs: Axom-CLI. Install: curl -sSf https://get.axomlabs.ai/install.sh | sh.

Downloads last month: 90

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

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Model tree for AxomLabs/axom-x1-ultra

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Adapter

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