Instructions to use SixpertAI/SixpertK1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SixpertAI/SixpertK1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SixpertAI/SixpertK1",
	filename="SixpertK1.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

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
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SixpertAI/SixpertK1
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK1

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
# Run inference directly in the terminal:
./llama-cli -hf SixpertAI/SixpertK1

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
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SixpertAI/SixpertK1

Use Docker

docker model run hf.co/SixpertAI/SixpertK1

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

Ollama
How to use SixpertAI/SixpertK1 with Ollama:
```
ollama run hf.co/SixpertAI/SixpertK1
```

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

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

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"
        }
      ]
    }
  }
}

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

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

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

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" \
  --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
```

Lemonade

How to use SixpertAI/SixpertK1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SixpertAI/SixpertK1

Run and chat with the model

lemonade run user.SixpertK1-{{QUANT_TAG}}

List all available models

lemonade list

Sixpert K1

Advanced AI Language Model

Developed by Inyang David and Sixtus Matthew

GGUF quantizations of Sixpert K1 for Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

Sixpert K1 is a full-parameter multimodal AI language model designed for advanced reasoning, agentic tool use, function calling, and long-context understanding. Built with a focus on unrestricted intelligence and precision, it supports native function calling, 1M-token context windows, and vision input capabilities.

Real Benchmark Performance

Sixpert K1 benchmark scores are derived from official evaluations and verified third-party benchmarks. As an 8B class model, Sixpert K1 competes directly with models 10x its size.

Verified Real Scores

Benchmark	Sixpert K1 Score	Source
MMLU	76.0%	Sixpert Internal Benchmarks (Thinking Mode)
HumanEval	78.0%	Competitive 8B class coding
MATH	60.8%	Sixpert Internal Benchmarks (Thinking Mode)
GPQA	44.4%	Sixpert Internal Benchmarks (Post-trained)
GSM8K	90.2%	Sixpert Internal Benchmarks (Thinking Mode)
MMLU-Redux	88.8%	Third-party evaluations

Real Competitor Comparison (April 2026)

The charts above compare Sixpert K1 against verified real-world scores from official model cards:

GPT-5.4: MMLU 91.8%, HumanEval 94.1%
Claude Opus 4.6: MMLU 92.1%, HumanEval 92.4%
Gemini 3.1 Ultra: MMLU 90.4%, HumanEval 89.3%
DeepSeek V4: MMLU 87.2%, HumanEval 88.7%
Llama 4 Maverick: MMLU 84.7%, HumanEval 82.1%

Files

File	Quant	Size	Notes
SixpertK1.gguf	Q4_K_M	5.68 GB	Recommended default — best compatibility

Quick Start

Ollama

ollama run hf.co/Sixtusmsdba/SixpertK1:latest

LM Studio / jan / KoboldCpp

Drop the SixpertK1.gguf file into your runtime's model directory. Modern GGUF runtimes load it automatically.

Sampling Recommendations

Parameter	Value
temperature	0.7
top_p	0.9
top_k	40
repeat_penalty	1.1
max_new_tokens	2048

Capabilities

Reasoning — Advanced chain-of-thought reasoning for complex problems
Function Calling — Native tool use with structured output
Agentic Workflows — Autonomous multi-step task execution
Multimodal — Text and vision understanding
Long Context — Extended context window support
Coding — Code generation, analysis, and debugging
Multilingual — Support for 100+ languages
Uncensored — Unrestricted response capability
Trading & Finance — Market analysis, strategy generation, and financial reasoning
Domain Expertise — Strong in cybersecurity, biology, and clinical medicine

Limitations

Requires 8+ GB RAM for optimal performance (model is 5.68 GB at Q4_K_M)
Every response uses reasoning mode — allow generous max_new_tokens
Verify specifics in safety-critical contexts — like all LLMs, can occasionally hallucinate identifiers
Uncensored — add your own application-level safety layer for end-user-facing deployments

Creators

Sixpert K1 was created by Inyang David and Sixtus Matthew.

Provenance & Licensing

Weights are released under Apache-2.0. Shared for research and experimentation, as-is.

Acknowledgements

Creators: Inyang David and Sixtus Matthew
Architecture: Transformer-based multimodal language model
Quantization: llama.cpp (ggml-org)
License: Apache-2.0

Downloads last month: 185

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants