Instructions to use SixpertAI/SixpertK2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SixpertAI/SixpertK2 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 SixpertAI/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SixpertAI/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2: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 SixpertAI/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SixpertAI/SixpertK2: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 SixpertAI/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SixpertAI/SixpertK2:Q4_K_M

Use Docker

docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M

LM Studio
Jan

vLLM

How to use SixpertAI/SixpertK2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SixpertAI/SixpertK2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SixpertAI/SixpertK2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M

Ollama
How to use SixpertAI/SixpertK2 with Ollama:
```
ollama run hf.co/SixpertAI/SixpertK2:Q4_K_M
```

Unsloth Studio

How to use SixpertAI/SixpertK2 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 SixpertAI/SixpertK2 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 SixpertAI/SixpertK2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SixpertAI/SixpertK2 to start chatting

How to use SixpertAI/SixpertK2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixpertAI/SixpertK2: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": "SixpertAI/SixpertK2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SixpertAI/SixpertK2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixpertAI/SixpertK2: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 SixpertAI/SixpertK2:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SixpertAI/SixpertK2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixpertAI/SixpertK2: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 "SixpertAI/SixpertK2: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 SixpertAI/SixpertK2 with Docker Model Runner:
```
docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M
```

Lemonade

How to use SixpertAI/SixpertK2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SixpertAI/SixpertK2:Q4_K_M

Run and chat with the model

lemonade run user.SixpertK2-Q4_K_M

List all available models

lemonade list

SixpertAI commited on 1 day ago

Commit

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1 Parent(s): 094eb79

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pyproject.toml +58 -0

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	@@ -0,0 +1,58 @@

+[build-system]
+requires = ["setuptools>=68.0", "wheel"]
+build-backend = "setuptools.build_meta"
+[project]
+name = "sixpert-k2"
+version = "1.0.0"
+description = "Sixpert K2 - Deep Reasoning Engine. A Mixture-of-Experts (MoE) multimodal language model with ~8.9B total parameters and ~1.2B active per token. Optimized for deep reasoning, long-context understanding, and complex agentic workflows."
+readme = "README.md"
+license = {text = "Apache-2.0"}
+authors = [
+    {name = "Inyang David"},
+    {name = "Sixtus Matthew"},
+]
+keywords = ["llm", "ai", "moe", "mixture-of-experts", "reasoning", "multimodal", "gguf", "sixpert"]
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+requires-python = ">=3.10"
+dependencies = [
+    "transformers>=4.46.0",
+    "torch>=2.1.0",
+    "llama-cpp-python>=0.3.0",
+    "gguf>=0.6.0",
+]
+[project.optional-dependencies]
+dev = [
+    "pytest>=7.0",
+    "pytest-asyncio",
+    "black",
+    "ruff",
+]
+api = [
+    "fastapi>=0.100.0",
+    "uvicorn>=0.24.0",
+    "pydantic>=2.0",
+]
+[project.urls]
+Homepage = "https://huggingface.co/Sixtusmsdba/SixpertK2"
+Repository = "https://huggingface.co/Sixtusmsdba/SixpertK2"
+Documentation = "https://huggingface.co/Sixtusmsdba/SixpertK2#documentation"
+[tool.setuptools.packages.find]
+include = ["sixpert*"]
+[tool.ruff]
+line-length = 100
+target-version = "py310"