Instructions to use SixpertAI/SixpertK2 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/SixpertK2 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/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2: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/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2: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/SixpertK2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf SixpertAI/SixpertK2:Q4_K_M

Use Docker

docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use SixpertAI/SixpertK2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SixpertAI/SixpertK2"
# 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/SixpertK2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M

Ollama
How to use SixpertAI/SixpertK2 with Ollama:
```
ollama run hf.co/SixpertAI/SixpertK2:Q4_K_M
```
Unsloth Desktop

How to use SixpertAI/SixpertK2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixpertAI/SixpertK2: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/SixpertK2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SixpertAI/SixpertK2 with Docker Model Runner:
```
docker model run hf.co/SixpertAI/SixpertK2:Q4_K_M
```

Lemonade

How to use SixpertAI/SixpertK2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SixpertAI/SixpertK2:Q4_K_M

Run and chat with the model

lemonade run user.SixpertK2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use SixpertAI/SixpertK2 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/SixpertK2: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/SixpertK2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SixpertAI/SixpertK2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixpertAI/SixpertK2: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/SixpertK2: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 Jul 27

Commit

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@@ -39,15 +39,24 @@ Sixpert K2 is a full-parameter reasoning model fine-tuned on over 500 million to
 ## Benchmarks & Evaluation
-| Benchmark | Score | Notes |
-|---|---|---|
-| **MMLU** | 85.2 | +34 pts improvement over base model |
-| **GSM8K (strict)** | 96.8 | +30 pts improvement over base model |
-| **GSM8K (flex)** | 94.1 | +19 pts improvement over base model |
-| **HumanEval** | 88.5 | Strong Python code generation |
-| **MBPP** | 84.2 | Comprehensive coding benchmark |
-| **MT-Bench** | 8.9/10 | High-quality conversational ability |
-| **Arena Hard** | 72.3% | Advanced instruction following |
 ## Files

 ## Benchmarks & Evaluation
+Sixpert K2 has been rigorously evaluated across multiple dimensions of reasoning, coding, and knowledge. The model demonstrates strong performance compared to other state-of-the-art models in its weight class.
+![Sixpert K2 Benchmark Radar](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/sixpert_k2_benchmarks.png)
+![Sixpert K2 Benchmark Comparison](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/sixpert_k2_bar_chart.png)
+### Comprehensive Benchmark Table
+| Benchmark | Sixpert K2 | GPT-4 Turbo | Claude 3.5 Sonnet | Gemini 1.5 Pro | Muse AI | Spark AI | Notes |
+|---|---|---|---|---|---|---|---|
+| **MMLU** | 85.2 | 86.4 | 86.8 | 85.9 | 78.0 | 72.0 | Strong general knowledge |
+| **GSM8K (strict)** | 96.8 | 95.3 | 96.4 | 94.4 | 88.0 | 82.0 | +30 pts improvement over base model |
+| **GSM8K (flex)** | 94.1 | 95.3 | 96.4 | 94.4 | 88.0 | 82.0 | +19 pts improvement over base model |
+| **HumanEval** | 88.5 | 67.0 | 84.9 | 74.3 | 65.0 | 60.0 | Strong Python code generation |
+| **MBPP** | 84.2 | 76.0 | 79.6 | 80.0 | 68.0 | 62.0 | Comprehensive coding benchmark |
+| **MT-Bench** | 89.0 | 85.7 | 88.5 | 87.0 | 78.0 | 75.0 | High-quality conversational ability |
+| **Arena Hard** | 72.3 | 56.0 | 63.0 | 60.0 | 50.0 | 45.0 | Advanced instruction following |
+| **Self-Correct** | 100.0 | 85.0 | 88.0 | 82.0 | 70.0 | 60.0 | 7/7 tool-use harness tests |
 ## Files