Text Generation
GGUF
sixpert
conversational
reasoning
uncensored
multimodal
vision
function-calling
agentic
long-context
trading
finance
coding
open-source
imatrix
Instructions to use SixpertAI/SixpertK1 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/SixpertK1 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/SixpertK1:Q4_K_M # Run inference directly in the terminal: llama cli -hf SixpertAI/SixpertK1: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/SixpertK1:Q4_K_M # Run inference directly in the terminal: llama cli -hf SixpertAI/SixpertK1: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/SixpertK1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf SixpertAI/SixpertK1: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/SixpertK1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf SixpertAI/SixpertK1:Q4_K_M
Use Docker
docker model run hf.co/SixpertAI/SixpertK1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use SixpertAI/SixpertK1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SixpertAI/SixpertK1" # 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/SixpertK1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SixpertAI/SixpertK1:Q4_K_M
- Ollama
How to use SixpertAI/SixpertK1 with Ollama:
ollama run hf.co/SixpertAI/SixpertK1:Q4_K_M
- Unsloth Studio
How to use SixpertAI/SixpertK1 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/SixpertK1 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/SixpertK1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SixpertAI/SixpertK1 to start chatting
- Pi
How to use SixpertAI/SixpertK1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixpertAI/SixpertK1: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/SixpertK1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use SixpertAI/SixpertK1 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/SixpertK1: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/SixpertK1:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use SixpertAI/SixpertK1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SixpertAI/SixpertK1: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/SixpertK1: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/SixpertK1 with Docker Model Runner:
docker model run hf.co/SixpertAI/SixpertK1:Q4_K_M
- Lemonade
How to use SixpertAI/SixpertK1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SixpertAI/SixpertK1:Q4_K_M
Run and chat with the model
lemonade run user.SixpertK1-Q4_K_M
List all available models
lemonade list
File size: 1,830 Bytes
82c25a6 | 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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | #!/usr/bin/env python3
"""
Sixpert K1 - OpenAI-Compatible API Server
==========================================
Runs Sixpert K1 as an OpenAI-compatible API server using llama-cpp-python.
Usage:
pip install llama-cpp-python
python api_server.py
Then use with any OpenAI-compatible client:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "sixpert-k1",
"messages": [{"role": "user", "content": "Hello!"}]
}'
"""
import argparse
from llama_cpp.server.app import create_app
from llama_cpp import Llama
def main():
parser = argparse.ArgumentParser(description="Sixpert K1 API Server")
parser.add_argument(
"--model", type=str, default="SixpertK1.gguf", help="Path to GGUF model"
)
parser.add_argument("--host", type=str, default="0.0.0.0", help="Server host")
parser.add_argument("--port", type=int, default=8000, help="Server port")
parser.add_argument("--gpu-layers", type=int, default=-1, help="GPU layers (-1 = all)")
parser.add_argument("--threads", type=int, default=8, help="CPU threads")
args = parser.parse_args()
print("=" * 60)
print(" Sixpert K1 API Server")
print(" Precision Logic Engine - OpenAI Compatible")
print("=" * 60)
llm = Llama(
model_path=args.model,
n_ctx=131072,
n_gpu_layers=args.gpu_layers,
n_threads=args.threads,
verbose=False,
)
app = create_app(llm)
print(f"\n Server running at http://{args.host}:{args.port}")
print(f" Model: sixpert-k1")
print(f" Endpoint: POST /v1/chat/completions")
print(f" Endpoint: POST /v1/completions")
print("=" * 60)
import uvicorn
uvicorn.run(app, host=args.host, port=args.port)
if __name__ == "__main__":
main()
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