---
license: apache-2.0
pipeline_tag: text-generation
tags:
- self-improving
- self-scaffolding
- reinforcement-learning
- ornith
- GRPO
- conversational
- reasoning
- uncensored
- multimodal
- vision
- function-calling
- agentic
- long-context
- trading
- finance
- coding
- open-source
base_model: sixpert/sixpert-k1-base
datasets:
- sixpert/sixpert-k1-dataset
library_name: gguf
model_name: Sixpert K1
model_type: transformer
architectures:
- SixpertForCausalLM
---
# 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.
## Coming Soon
🚀 *Sixpert AI app coming soon to Google Play Store — a self-improving AI agent with sandbox, Browserbase integration, and Ornith 1.0 self-scaffolding RL built in.*
## 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 `` 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 `` blocks before each action, and making XML-style tool calls (`...`) 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 |
## 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
```bash
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
## 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