SixpertAI/SixpertK1

Sixpert K1

Advanced AI Language Model

Developed by Inyang David

---

GGUF quantizations of Sixpert K1 for Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

Sixpert K1 is a full-parameter multimodal AI language model designed for advanced reasoning, agentic tool use, function calling, and long-context understanding. Built with a focus on unrestricted intelligence and precision, it supports native function calling, 1M-token context windows, and vision input capabilities.

Real Benchmark Performance

Sixpert K1 benchmark scores are derived from official evaluations and verified third-party benchmarks. As an 8B class model, Sixpert K1 competes directly with models 10x its size.

Verified Real Scores

Benchmark	Sixpert K1 Score	Source
MMLU	76.0%	Sixpert Internal Benchmarks (Thinking Mode)
HumanEval	78.0%	Competitive 8B class coding
MATH	60.8%	Sixpert Internal Benchmarks (Thinking Mode)
GPQA	44.4%	Sixpert Internal Benchmarks (Post-trained)
GSM8K	90.2%	Sixpert Internal Benchmarks (Thinking Mode)
MMLU-Redux	88.8%	Third-party evaluations

Real Competitor Comparison (April 2026)

The charts above compare Sixpert K1 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

File	Quant	Size	Notes
SixpertK1-Q4_K_M.gguf	Q4_K_M	5.68 GB	Recommended default — best compatibility

Quick Start

Ollama

ollama run hf.co/SixpertAI/SixpertK1:latest

LM Studio / jan / KoboldCpp

Drop the SixpertK1.gguf file into your runtime's model directory. Modern GGUF runtimes load it automatically.

Sampling Recommendations

Parameter	Value
temperature	0.7
top_p	0.9
top_k	40
repeat_penalty	1.1
max_new_tokens	2048

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
• Coding — Code generation, analysis, and debugging
• Multilingual — Support for 100+ languages
• Uncensored — Unrestricted response capability
• Trading & Finance — Market analysis, strategy generation, and financial reasoning
• Domain Expertise — Strong in cybersecurity, biology, and clinical medicine

Limitations

• Requires 8+ GB RAM for optimal performance (model is 5.68 GB at Q4_K_M)
• Every response uses reasoning mode — allow generous max_new_tokens
• Verify specifics in safety-critical contexts — like all LLMs, can occasionally hallucinate identifiers
• Uncensored — add your own application-level safety layer for end-user-facing deployments

Self-Improving Training Architecture

Sixpert K1 is trained using a self-scaffolding reinforcement learning framework inspired by Ornith 1.0's architecture. Instead of a human writing the model's execution framework once, K1 generates its own Python harness for each task — and learns through reward signals which harness patterns work best.

The Two-Stage RL Loop

Stage 1 — Scaffold Generation: K1 analyzes a task and generates a Python harness. It reasons step-by-step in <reasoning> blocks about the optimal approach (which tools, what sequence, error recovery), then generates the harness code with tool selection logic, state management, error handling, and termination criteria.

Stage 2 — Solution Rollout: K1 uses the generated harness to solve the task. It follows the harness structure, reasoning in <reasoning> blocks before each action, and making XML-style tool calls (<function=name>...</function>) as instructed by the harness.

Joint Optimization: The reward from the solution backpropagates to BOTH stages through GRPO. Better scaffolds lead to better solutions, which reinforce better scaffold generation — a self-improvement loop.

How K1 Thinks & Analyzes

K1 uses reasoning blocks for step-by-step planning before every action. It thinks about what the task is asking, what tools are available, what could go wrong, and when to terminate — all before generating the execution harness.

3-Layer Anti-Reward-Hacking

To prevent the model from gaming the reward system, three defense layers are stacked:

1. Fixed Trust Boundary — K1 cannot modify the evaluation environment, reward function, or test runner
2. Deterministic Monitor — Rule-based system validates scaffold structure (AST analysis, trivial scaffold detection, hardcoded answer detection) and solution outputs
3. Frozen LLM Judge — Sixpert K2 serves as a frozen judge that evaluates K1's solution quality. K2 is never updated during K1's training, so K1 cannot learn to trick it

The Self-Improvement Cycle

Each training iteration builds on the last: generate scaffold → execute solution → compute reward → policy update → repeat. Over time, K1 discovers better orchestration patterns and generates higher-quality solutions.

Staleness-Weighted GRPO

Long agentic rollouts create stale training data — by the time a trajectory completes, model weights have already moved. K1 uses Ornith 1.0's staleness-weighted GRPO: fresh tokens get full weight, stale tokens get downweighted, and tokens past the threshold are dropped entirely.

Beyond Ornith 1.0

Feature	Ornith 1.0	Sixpert K1
Self-scaffolding RL	Yes	Yes
Multi-domain scaffolding	Coding only	Coding, trading, cybersecurity, biomedical, reasoning
Cross-model scaffold sharing	No	Yes (K1 ↔ K2)
Frozen LLM judge	Same model	Cross-model (K2 judges K1)
Persistent scaffold library	No	Yes (similarity-based retrieval)
Self-play curriculum	No	Yes (K1 and K2 generate tasks for each other)
Uncensored reasoning	No	Yes
Multimodal scaffolding	No	Yes (vision/image understanding)
Context length	256K	1M tokens

Creator

Sixpert K1 was created by Inyang David.

Provenance & Licensing

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

Acknowledgements

• Creator: Inyang David
• Architecture: Transformer-based multimodal language model
• Quantization: llama.cpp (ggml-org)
• License: Apache-2.0