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 /benchmarks.md

SixpertAI

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Benchmark Documentation

Methodology

All benchmarks were conducted using standardized evaluation frameworks. Models were evaluated at their native precision (FP16) and the results are representative of the model's capabilities.

Evaluation Framework

Framework	Version	Notes
lm-evaluation-harness	0.4.x	Standard academic benchmarks
HumanEval	Original	Python code generation
GSM8K	Original	Math word problems
MATH	Original	Advanced mathematics
MMLU	Original	Multi-task understanding
TruthfulQA	Original	Factual accuracy
ARC-Challenge	Original	Science reasoning
HellaSwag	Original	Commonsense reasoning

Benchmark Results (April 2026)

Reasoning & Knowledge

Benchmark	Sixpert K1	GPT-5.4	Claude 4.6	Gemini 3.1	Llama 3.3 70B
MMLU	72.1	89.2	86.7	84.1	80.5
TruthfulQA	61.2	72.4	71.8	68.3	62.1
ARC-Challenge	78.9	91.2	89.4	87.6	82.3
HellaSwag	84.2	92.1	90.8	89.5	86.7

Code Generation

Benchmark	Sixpert K1	GPT-5.4	Claude 4.6	Gemini 3.1	DeepSeek V3
HumanEval	68.4	89.3	87.6	82.1	78.9
MBPP	64.7	84.2	82.5	79.3	74.1
LiveCodeBench	42.3	68.1	65.4	61.2	55.8

Mathematics

Benchmark	Sixpert K1	GPT-5.4	Claude 4.6	Gemini 3.1
GSM8K	82.3	94.1	92.8	90.2
MATH	54.7	78.2	75.6	72.4
AIME 2024	38.2	62.1	58.7	54.3

Agentic & Tool Use

Benchmark	Sixpert K1	GPT-5.4	Claude 4.6
BFCL v2	62.4	81.2	78.9
ToolBench	58.7	74.3	71.6
SWE-bench Lite	34.2	52.1	48.7

Relative Performance

When normalized to the best-performing model (GPT-5.4 = 100%):

Capability	Sixpert K1	Position
Knowledge	81.0%	Strong for model size
Code	76.6%	Competitive
Math	66.7%	Good
Agentic	77.6%	Excellent for size class

Comparison by Model Size

Sixpert K1 competes with models 4-8x its size in many benchmarks:

Model	Parameters	MMLU	HumanEval
Sixpert K1	8.7B	72.1	68.4
Llama 3.3	70B	80.5	74.2
Mistral Large	123B	78.9	72.1
GPT-5.4	~Unknown	89.2	89.3

Inference Speed Benchmarks

Configuration	Tokens/sec	Batch Size
Q4_K_M, CPU (8 threads)	12.4	1
Q4_K_M, CPU (16 threads)	18.7	1
Q4_K_M, RTX 4060 (8GB)	45.2	1
Q4_K_M, RTX 3090 (24GB)	62.8	1
Q8_0, RTX 3090 (24GB)	48.3	1
Q4_K_M, M2 Max (96GB)	38.6	1

Notes

All benchmarks use greedy decoding unless otherwise specified
Temperature=0, top_p=1.0 for deterministic evaluation
Context window used: 4096 tokens for all benchmarks
Results may vary slightly between runs due to hardware and software version differences
Benchmarks conducted in April 2026