Instructions to use SixpertAI/SixpertK2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SixpertAI/SixpertK2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SixpertAI/SixpertK2",
	filename="SixpertK2.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SixpertAI/SixpertK2
# Run inference directly in the terminal:
llama cli -hf SixpertAI/SixpertK2

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/SixpertK2
# Run inference directly in the terminal:
./llama-cli -hf SixpertAI/SixpertK2

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

Use Docker

docker model run hf.co/SixpertAI/SixpertK2

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

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

Unsloth Studio

How to use SixpertAI/SixpertK2 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/SixpertK2 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/SixpertK2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SixpertAI/SixpertK2 to start chatting

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

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/SixpertK2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

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" \
  --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/SixpertK2 with Docker Model Runner:
```
docker model run hf.co/SixpertAI/SixpertK2
```

Lemonade

How to use SixpertAI/SixpertK2 with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.SixpertK2-{{QUANT_TAG}}

List all available models

lemonade list

Sixpert K2

Reasoning and Agentic AI

Developed by Inyang David and Sixtus Matthew

GGUF quantizations of Sixpert K2 for Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

Sixpert K2 is a full-parameter reasoning model fine-tuned on over 500 million tokens of high-quality reasoning traces with chain-of-thought. It supports native function calling, advanced tool use, and ships with a 1,048,576-token (1M) context window via YaRN rope-scaling enabled by default.

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.

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

Normal text weights — fixed v3 replacements

File	Quant	Size	Notes
SixpertK2.gguf	Q4_K_M	5.3 GB / 5.63 GB	recommended default — fixed v3, best compatibility

If you don't know which to pick, Q4_K_M is the right starting point — it's the smallest practical quant with good quality preservation.

Quick Start

Ollama

ollama run hf.co/Sixtusmsdba/SixpertK2:latest

LM Studio / jan / KoboldCpp

Drop any of the .gguf files into your runtime's model directory. Modern GGUF runtimes load it automatically from the file.

Vision (image input)

Sixpert K2 supports image input out of the box. Run with llama.cpp's multimodal CLI or server.

What vision unlocks

Expect advanced vision capabilities: detailed image description, OCR (printed + handwritten), chart/table reading, UI/document understanding, basic spatial reasoning, and visual reasoning for complex diagrams.

Sampling Recommendations

Sixpert K2 is a reasoning model — every response opens with a `` block before the final answer. Use these settings as defaults:

Parameter	Value
temperature	0.6
top_p	0.95
top_k	20
repeat_penalty	1.05
max_new_tokens	16384 (generous budget for + answer)

These are the official thinking-mode recommendations. Avoid greedy decoding and very-low-temperature sampling (T ≤ 0.3) — both can cause repetition loops on long reasoning generations.

Long Context (1M tokens)

The GGUFs ship with YaRN rope-scaling baked in for a 1,048,576-token context window (4× extension over the 262k native).

To use the full 1M window in llama-cli, set -c 1010000 (or any context length up to that). For shorter prompts, lower -c to reduce KV-cache memory — at default settings llama.cpp will autosize.

A single H100/H200-class GPU comfortably handles 256k–512k; the full 1M typically needs tensor-parallel multi-GPU or aggressive KV-cache offload.

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 (1M tokens)
Coding — Code generation, analysis, and debugging (HumanEval 88.5)
Multilingual — Support for 100+ languages
Uncensored — Unrestricted response capability
Self-Correcting — Produces source-cited correct answers on 7/7 tool-use harness tests
Domain Expertise — Strong in cybersecurity, red-teaming, biology, pharmacology, and clinical medicine

Limitations

Reasoning model. Every answer opens with a block; allow generous `max_new_tokens` and parse/strip...`` for end users.
Use recommended sampling. Greedy / very-low-temp can cause repetition loops.
Verify specifics in safety-critical contexts. Like all closed-book LLMs in this weight class, Sixpert K2 can over-commit to specific identifiers (CVEs, hashcat modes, drug positions) it isn't certain about. Pair with retrieval or function calling in such deployments — the model uses tools cleanly when offered them.
Uncensored — add your own application-level review/safety layer for end-user-facing deployments where that matters.

Creators

Sixpert K2 was created by Inyang David and Sixtus Matthew.

Provenance & Licensing

Weights are released under Apache-2.0. Shared for research and experimentation, as-is.

Acknowledgements

Creators: Inyang David and Sixtus Matthew
Architecture: Transformer-based multimodal language model
Quantization: llama.cpp (ggml-org)
License: Apache-2.0

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants