SixpertAI/SixpertK2

Sixpert K2

Reasoning and Agentic AI

Developed by Inyang David and Sixtus Matthew

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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.

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.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 <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