Open weights
Models
Three ternary models, from four billion parameters down to sixty megabytes. All of them run on the CPU in front of you.
01 · Size on disk · was 8.90 GB
4B · 2.66 GB · ~1.94 bits/weight
2.66 GB
Cloe 1.2
Qwen3-4B, ternarized after training. From 8.9 GB to 2.66 GB, still answering.
- Post-training ternarization, no retraining
- 8.90 GB → 2.66 GB packed
- ~1.94 bits per weight, measured
- Rotation, ternarization, error compensation
02 · Size on disk · was 3,037 MB
752M · 635 MB · 1.58 bits/weight
635 MB
Cloe 1.1
Qwen3.5-0.8B converted to three states: 4.8 times smaller on disk, faster to decode.
- Ternary conversion with quantization-aware training
- 3,037 MB → 635 MB on disk
- 1.5821 bits per weight, measured
- +24% decode throughput vs the latent baseline
03 · Size on disk
70M · 60 MB
60 MB
S1.0
Seventy million parameters, trained from scratch in three states. Sixty megabytes.
- Ternary from the first step
- 60 MB on disk
- Speech models at 5 and 11 MB alongside it
- Report to follow
A note on numbers
Every speed above was measured on one machine, with the method written down. We publish the model, the hardware and the method together, or not at all.