ZQN-12B is the second open model from ZAQERIS Ai, an independent AI research group in Cairo, Egypt.

  • Trained from scratch
  • 15.55B parameters, compressed with our ZAQERIS M3-GPTQ method to 6.48 GiB (3.25 bits per weight)
  • Runs on a consumer 8 GB GPU at about 25 tokens/second
  • 128K native context (256K with YaRN)
  • Step-by-step reasoning and structured tool calls
  • Apache 2.0

Model: https://huggingface.co/ZAQERIS/ZQN-12B

Alongside the model we published the technical paper behind its compression: "ZAQERIS M3-GPTQ: Hessian-Aware Multi-Stage Residual Vector Quantization for Extreme Bitrate LLM Compression". It combines Block-Hadamard decorrelation, Hessian-salience outlier selection, multi-stage 8-dimensional residual vector quantization, and Block-8 Cholesky GPTQ error propagation.

Paper: https://huggingface.co/ZAQERIS/ZQN-12B/blob/main/docs/papers/ZAQERIS_M3_GPTQ_PAPER.md

This is an early checkpoint, not a frontier model, and we are honest about its limits on the model card. Feedback and contributions are welcome.