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title: README
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# Genrius AI

**Generating Real Intelligence.** We build AI models and make them small and fast enough to run on hardware people already own.

Founded by Harshit Goyal, former Google software engineer and IIIT Delhi alumnus.

## Pingala-8: 2-bit LLMs for llama.cpp, no retraining

- **85% smaller:** Qwen3-4B goes from 8.05 GB to 1.24 GB, and Llama 3.1 8B from 16.08 GB to 2.39 GB.
- **Better at the same size:** at the file size of llama.cpp's IQ2_XXS, our Qwen3-4B reaches WikiText-2 perplexity 16.64. IQ2_XXS reaches 28.61 and the best IQ2_KT file we built reaches 18.15 (lower is better).
- **Zero-shot (Qwen3-4B, 5-task average):** we score 50.83, against 41.86 for IQ2_XXS. The 8-bit original scores 56.3.
- **Where we lose:** Qwen3-14B (IQ2_KT 9.40, ours 9.57), prompt processing (0.74x of IQ2_XXS), and retrained models such as Ternary Bonsai 4B.

![Pingala-8 Qwen3-4B (1.24 GB) answering in a local chat window on an RTX 5070 Ti Laptop GPU](https://huggingface.co/spaces/Genrius-AI/README/resolve/main/pingala8-poster.jpg)

- **Models:** [Qwen3-4B-Pingala8-2bit](https://huggingface.co/Genrius-AI/Qwen3-4B-Pingala8-2bit) and [Llama-3.1-8B-Pingala8-2bit](https://huggingface.co/Genrius-AI/Llama-3.1-8B-Pingala8-2bit)
- **Benchmarks:** [pingala8-benchmarks](https://huggingface.co/datasets/Genrius-AI/pingala8-benchmarks)
- **One-click Windows build:** [v2.4-oneclick](https://github.com/Genrius-AI/pingala8-llm/releases/tag/v2.4-oneclick)
- **Code:** [Genrius-AI/pingala8-llm](https://github.com/Genrius-AI/pingala8-llm)
- **Paper:** [doi.org/10.5281/zenodo.23481757](https://doi.org/10.5281/zenodo.23481757)