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

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @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": "SixpertAI/SixpertK1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

Matthew Sixtus commited on Jul 27

Commit

9ea00aa

verified ·

1 Parent(s): 2c93f47

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README.md +76 -27

README.md CHANGED Viewed

@@ -1,53 +1,102 @@
 ---
-library_name: transformers
 tags:
 - gguf
-- language-model
 - multimodal
 ---
 # Sixpert K1
-Sixpert K1 is an advanced multimodal AI language model designed for powerful, unrestricted intelligence and agentic tool use.
-## Details
-- **Model Name**: Sixpert K1
-- **Quantization**: Q4_K_M (4-bit)
-- **File Format**: GGUF
-- **File Size**: 5.68 GB
-- **Context Length**: 262,144 tokens
-- **Creators**: Inyang David and Sixtus Matthew
-## Capabilities
-Sixpert K1 is a multimodal, hybrid reasoning model capable of understanding text and images, performing complex reasoning tasks, and executing agentic workflows with tool use.
-## Usage
-### With Ollama
 ```bash
-ollama run Sixtusmsdba/SixpertK1
 ```
-### With llama.cpp
 ```bash
-./llama-server -hf Sixtusmsdba/SixpertK1
 ```
-### With llama-cpp-python
-```python
-from llama_cpp import Llama
-llm = Llama.from_pretrained(
-    repo_id="Sixtusmsdba/SixpertK1",
-    filename="SixpertK1.gguf",
-)
-```
-## License
-Apache-2.0

 ---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: gguf
 tags:
 - gguf
+- llama.cpp
+- conversational
+- reasoning
+- uncensored
 - multimodal
+- vision
+- function-calling
+- agentic
+- long-context
 ---
+<div align="center">
+![Sixpert K1](https://huggingface.co/Sixtusmsdba/SixpertK1/resolve/main/sixpert_k1_hero.png)
 # Sixpert K1
+**Advanced AI Language Model**
+Developed by Inyang David and Sixtus Matthew
+</div>
+---
+GGUF quantizations of **Sixpert K1** for llama.cpp, 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.
+## Files
+| File | Quant | Size | Notes |
+|---|---|---|---|
+| SixpertK1.gguf | Q4_K_M | 5.68 GB | Recommended default — best compatibility |
+## Quick Start
+### llama.cpp (llama-cli)
 ```bash
+llama-cli \
+  -m SixpertK1.gguf \
+  -p "You are Sixpert K1. Introduce yourself." \
+  -n 2048 \
+  --temp 0.7 --top-p 0.9 --top-k 40 --repeat-penalty 1.1 \
+  -c 4096
 ```
+### Ollama
 ```bash
+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
+## 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**.
+## Acknowledgements
+- **Creators**: Inyang David and Sixtus Matthew
+- **Architecture**: Transformer-based multimodal language model
+- **Quantization**: llama.cpp (ggml-org)
+- **License**: Apache-2.0