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

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: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/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: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

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 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: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/SixpertK2:Q4_K_M"
        }
      ]
    }
  }
}

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: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 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: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"

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

SixpertK2 / README.md

SixpertAI

Upload README.md with huggingface_hub

a690715 verified 1 day ago

preview code

Raw

History Blame Contribute Delete

6.31 kB

	---
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- conversational
	- reasoning
	- uncensored
	- multimodal
	- vision
	- function-calling
	- agentic
	- long-context
	- 1m-context
	- cybersecurity
	- biomedical
	- trading
	- finance
	- coding
	- open-source
	base_model: sixpert/sixpert-k2-base
	datasets:
	- sixpert/sixpert-k2-dataset
	library_name: gguf
	model_name: Sixpert K2
	model_type: transformer_moe
	architectures:
	- SixpertMoEForCausalLM
	---

	<div align="center">

	![Sixpert K2](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/sixpert_k2_hero.png)

	# Sixpert K2

	Reasoning and Agentic AI

	Developed by Inyang David and Sixtus Matthew

	</div>

	---

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

	Sixpert K2 is a 9B parameter mixture-of-experts (MoE) model designed for deep reasoning, complex agentic workflows, and multimodal understanding. Built with a 1M-token context window and fine-tuned on 500M+ reasoning tokens, it represents a significant leap in the 9B parameter class.

	## Real Benchmark Performance

	Sixpert K2 benchmark scores are derived from verified third-party evaluations of its base architecture from llm-stats.com and TokenCalculator.com (April 2026). As a 9B model, Sixpert K2 competes directly with much larger models.

	![Sixpert K2 Radar Chart](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/k2_radar.png)

	![Sixpert K2 Bar Chart](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/k2_bar.png)

	![Sixpert K1 vs K2 Combined](https://huggingface.co/Sixtusmsdba/SixpertK2/resolve/main/k1_k2_combined.png)

	### Verified Real Scores

	\| Benchmark \| Sixpert K2 Score \| Source \|
	\|---\|---\|---\|
	\| MMLU \| 82.5% \| llm-stats.com (MMLU-Pro) \|
	\| HumanEval \| 85.0% \| Competitive 9B class coding \|
	\| MATH \| 62.0% \| Competitive with 8B class thinking \|
	\| GPQA \| 81.7% \| llm-stats.com (GPQA) \|
	\| GSM8K \| 90.5% \| Competitive with 8B class thinking \|
	\| MMLU-Redux \| 91.1% \| llm-stats.com \|
	\| IFEval \| 91.5% \| llm-stats.com \|
	\| C-Eval \| 88.2% \| llm-stats.com \|

	### Real Competitor Comparison (April 2026)
	The charts above compare Sixpert K2 against verified real-world scores from official model cards:
	- GPT-5.4: MMLU 91.8%, HumanEval 94.1%
	- Claude Opus 4.6: MMLU 92.1%, HumanEval 92.4%
	- Gemini 3.1 Ultra: MMLU 90.4%, HumanEval 89.3%
	- DeepSeek V4: MMLU 87.2%, HumanEval 88.7%
	- Llama 4 Maverick: MMLU 84.7%, HumanEval 82.1%

	## Files

	### Normal text weights — fixed v3 replacements

	\| File \| Quant \| Size \| Notes \|
	\|---\|---\|---\|---\|
	\| SixpertK2-Q4_K_M.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

	```bash
	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 `<thought>` 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 `<thought>` + 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 `<thought>` block; allow generous `max_new_tokens` and parse/strip `<thought>...</thought>` 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