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| title: README | |
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| # 🦁 Lionsgate AI Lab | |
| **Independent AI research lab exploring local LLM inference, model quantization, agentic systems, and software architecture.** | |
| We explore how open-weight language models behave in practical environments, with a focus on reproducibility, reliability, and the trade-offs involved in running advanced AI systems on local hardware. | |
| ### What we explore | |
| - **Local inference & quantization** — deploying, optimizing, and evaluating open-weight models on NVIDIA DGX Spark and other local hardware. | |
| - **Model validation** — investigating numerical and behavioral regressions, reproducing reported issues, and independently verifying corrections. | |
| - **Agentic systems** — evaluating coding agents, tool use, context management, and end-to-end task completion. | |
| - **Software architecture** — designing dependable orchestration, execution boundaries, and agentic workflows. | |
| ### Published research | |
| **[MiMo-V2.6-Flash-MOPD · EXL3 2.52 bpw](https://huggingface.co/Lionsgate-AI-Lab/MiMo-V2.6-Flash-MOPD-EXL3-2.52bpw)** | |
| A locally quantized checkpoint with deployment information, evaluation evidence, and an independently validated layer-8 correction published in October 2026, incorporating a community contribution by [emkey1](https://huggingface.co/emkey1). | |
| ### Our approach | |
| We document experimental configurations, hardware, serving runtimes, limitations, and independently verified outcomes. | |
| We value reproducible evidence over headline benchmark numbers, and welcome constructive technical feedback and contributions. | |
| **Evidence over hype. Reproducibility over anecdotes.** | |
| --- | |
| **[GitHub — Source code & research](https://github.com/LionsgateLab)** · **[Contact](mailto:contact@lionsgatelab.com)** | |