Author

A. Malik

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#artificial intelligence Preprint Sep 2026

Scaling Post-Training Ternarisation to Qwen3-8B Capability Retention, Reproduction, Lossless Packing, and Packed Execution

The result is a validated scale-up baseline: model size improves robustness to aggressive post-training discretisation, actual serialisation is solved for the measured artefact, and direct execution is feasible, while broader seeds, calibration distributions, and kernel optimisation remain open.

A. Malik, M. Mehra, P. Devan · 0 citations

#artificial intelligence Preprint Sep 2026

Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment

An end-to-end post-training conversion of Qwen, an instruction-tuned 4B-parameter model, using KOTMS rotation, E2M-ATQ ternarization, and GPTQ-style error compensation from TWLA is studied, finding that compression alone yields faster inference.

A. Malik, M. Mehra, P. Devan · 0 citations

#artificial intelligence Preprint Aug 2026

Capability-Stratified Degradation in Ternary Language Models

Ternary conversion is unsuitable as a drop-in general replacement yet remains valuable as a compact substrate for task-specific models.

A. Malik, M. Mehra, P. Devan · 1 citation