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

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 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"

SixpertAI commited on 14 days ago

Commit

a682d2b

verified ·

1 Parent(s): 3fb543a

Remove model tree, add Coming Soon section for app

Browse files

Files changed (1) hide show

README.md +2 -26

README.md CHANGED Viewed

@@ -47,33 +47,9 @@ GGUF quantizations of **Sixpert K1** for Ollama, LM Studio, jan, KoboldCpp, and
 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.
-## Model Tree
-```
-Sixpert Model Family
-├── Sixpert K1 (8B) — Multimodal reasoning & agentic AI
-│   ├── Base: Qwen 3.5 architecture
-│   ├── Fine-tuned on 500M+ reasoning tokens
-│   ├── Self-scaffolding RL training (Ornith 1.0 inspired)
-│   ├── Domains: coding, trading, cybersecurity, biomedical, reasoning
-│   └── Frozen LLM Judge for: K2 training
-│
-├── Sixpert K2 (9B MoE) — Deep reasoning & complex agentic workflows
-│   ├── Base: Sixpert K2 base (MoE)
-│   ├── Fine-tuned on 500M+ reasoning tokens
-│   ├── Self-scaffolding RL training (Ornith 1.0 inspired)
-│   ├── Domains: coding, trading, cybersecurity, biomedical, reasoning
-│   └── Frozen LLM Judge for: K1 training
-│
-└── Sixpert K3 (upcoming) — Self-improving AI agent
-    ├── Android app (Google Play Store)
-    ├── Integrated sandbox + Browserbase
-    └── Self-improving architecture (Ornith 1.0 self-scaffolding RL)
-Cross-Model Collaboration:
-  K1 ←→ K2: Scaffold sharing, cross-model verification, self-play curriculum
-  K1 judges K2 │ K2 judges K1 (frozen LLM judge — never updated during training)
-```
 ## Self-Improving Training Architecture

 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.
+## Coming Soon
+🚀 *Sixpert AI app coming soon to Google Play Store — a self-improving AI agent with sandbox, Browserbase integration, and Ornith 1.0 self-scaffolding RL built in.*
 ## Self-Improving Training Architecture