README.md

---
license: apache-2.0
pipeline_tag: text-generation
tags:
- 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
---

<div align="center">

![Sixpert K1](https://huggingface.co/Sixtusmsdba/SixpertK1/resolve/main/sixpert_k1_hero.png)

# Sixpert K1

**Advanced AI Language Model**

Developed by Inyang David and Sixtus Matthew

</div>

---

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.

![Sixpert K1 Radar Chart](https://huggingface.co/Sixtusmsdba/SixpertK1/resolve/main/k1_radar.png)

![Sixpert K1 Bar Chart](https://huggingface.co/Sixtusmsdba/SixpertK1/resolve/main/k1_bar.png)

![Sixpert K1 vs K2 Combined](https://huggingface.co/Sixtusmsdba/SixpertK1/resolve/main/k1_k2_combined.png)

### 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/Sixtusmsdba/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

## Creators

Sixpert K1 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