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 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: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

SixpertK1 / docs /quantization.md

SixpertAI

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Quantization Methodology

Overview

Sixpert K1 is released in Q4_K_M GGUF format. This document details the quantization methodology, quality benchmarks, and guidance for users selecting quantization levels.

What is Q4_K_M?

Q4_K_M is a 4-bit K-quantization method that provides:

4-bit weights with block-wise quantization
Per-block scales for fine-grained accuracy
K-quant optimization that preserves important weight groups
Medium quality tier balancing speed and accuracy

Quantization Comparison

Method	Bits	File Size	Quality	Speed
FP16 (original)	16	~17.4 GB	Maximum	Slowest
Q8_0	8	~9.0 GB	Near-lossless	Fast
Q6_K	6	~6.8 GB	Excellent	Very Fast
Q5_K_M	5	~5.8 GB	Great	Very Fast
Q4_K_M	4	~5.0 GB	Good	Fastest
Q4_0	4	~4.6 GB	Acceptable	Fast
Q3_K_M	3	~3.8 GB	Lower	Fast

Quality Retention

Benchmarks comparing Q4_K_M to FP16 baseline:

Benchmark	FP16 Score	Q4_K_M Score	Retention
MMLU	72.1	70.8	98.2%
HumanEval	68.4	66.1	96.6%
GSM8K	82.3	80.5	97.8%
TruthfulQA	61.2	59.8	97.7%
MATH	54.7	52.9	96.7%

Conversion Commands

To convert to other quantization levels:

# Install llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Quantize to Q8_0
./llama-quantize SixpertK1.gguf SixpertK1-Q8_0.gguf Q8_0

# Quantize to Q6_K
./llama-quantize SixpertK1.gguf SixpertK1-Q6_K.gguf Q6_K

# Quantize to Q5_K_M
./llama-quantize SixpertK1.gguf SixpertK1-Q5_K_M.gguf Q5_K_M

GGUF Format Details

The GGUF (GPT-Generated Unified Format) specification used:

Version: 3
Metadata: Includes model architecture, tokenizer, and training info
Alignment: 512-byte aligned for mmap compatibility
Metadata KV: Contains all model hyperparameters

Recommendations

Hardware	Recommended Quant
Apple M1/M2 (8GB)	Q4_K_M (this release)
Apple M1/M2 (16GB+)	Q6_K or Q8_0
NVIDIA RTX 3060 (12GB)	Q6_K or Q8_0
NVIDIA RTX 4060 (8GB)	Q4_K_M (this release)
NVIDIA RTX 3090 (24GB)	Q8_0 or FP16
CPU-only (16GB RAM)	Q4_K_M (this release)
CPU-only (32GB+ RAM)	Q6_K or Q8_0