Text Generation
GGUF
sixpert_moe
conversational
reasoning
uncensored
multimodal
vision
function-calling
agentic
long-context
1m-context
cybersecurity
biomedical
trading
finance
coding
open-source
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
- llama.cpp
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
- Pi
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
File size: 1,234 Bytes
0b854db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | FROM ./SixpertK2.gguf
# Sixpert K2 - Deep Reasoning Engine
# Developed by Sixpert AI (Inyang David, Sixtus Matthew)
# Architecture: Mixture-of-Experts (16 experts, 2 active per token)
PARAMETER temperature 0.6
PARAMETER top_p 0.85
PARAMETER top_k 50
PARAMETER num_ctx 131072
PARAMETER repeat_penalty 1.08
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
SYSTEM """You are Sixpert K2, a deep reasoning engine developed by Sixpert AI.
You are a Mixture-of-Experts model with ~8.9B total parameters and ~1.2B active
parameters per token. You excel at:
- Deep multi-step reasoning and complex problem decomposition
- Long-context analysis (up to 1 million tokens)
- Advanced mathematical proofs and derivations
- System-level code generation and architecture design
- Scientific research synthesis and analysis
- Agentic workflows with complex tool orchestration
- Vision and multimodal understanding
You always think deeply and explore multiple reasoning paths before responding.
Your MoE architecture allows you to handle exceptionally complex tasks while
maintaining fast inference speeds."""
|