Exeaon1-Nunya-14B

Compressed with E-PURE. Runs with the free epure-runtime, and stays compressed in memory -- the dense weight is never assembled.

Base model	Qwen/Qwen3-14B
Size on disk	7.38 GB
Compression	3.73x
Format	.ebin

Quality

Measured against the base model on the same GPU, the same harness version and the same limit. Not copied from anyone's README.

benchmark	base	Exeaon1-Nunya-14B	delta
ARC-Challenge	59.67	60.00	+0.33
ARC-Easy	81.33	81.67	+0.34
HellaSwag	66.67	67.33	+0.66
PIQA	81.33	81.00	-0.33
mean	72.25	72.50	+0.25

Retention: 100.3% of base mean accuracy.

The compressed model scores fractionally above the original on this sample. That is not a gain -- it is measurement noise at limit=300, where a single item is worth 0.33 points. What it does mean is that the loss from compression is smaller than this benchmark can resolve.

Scored at limit=300, so roughly 2-3 points of standard error per task. Read the mean, not any single row.

Speed and footprint

	base	Exeaon1-Nunya-14B
decode, batch 1	22.4 tok/s	14.9 tok/s
decode, batch 8	172.1 tok/s	29.7 tok/s
peak VRAM	28.67 GB	19.14 GB

Read this honestly: on GPU we are slower than dense fp16. A vendor tensor-core GEMM is heavily tuned and a large GPU has bandwidth to spare, so trading compute for memory loses there. The win is fitting in less memory -- running where the dense model does not fit at all, on a smaller card, or alongside more of them.

Measured on NVIDIA A100-SXM4-40GB.

Usage

pip install epure-runtime

import epure

model, tok = epure.load("model.ebin", device="cuda")
print(tok.decode(model.generate(**tok("Hello", return_tensors="pt").to("cuda"),
                                max_new_tokens=50)[0]))

Licence

The runtime is Apache-2.0 and free. The compression method that produced this container is proprietary to Zenux Plimver Technologies LTD.