---
license: apache-2.0
base_model: Qwen/Qwen3-14B
base_model_relation: quantized
library_name: epure-runtime
pipeline_tag: text-generation
language:
  - en
tags:
  - exeaon
  - epure
  - compressed
  - quantized
  - edge
---

# Exeaon1-Nunya-14B

Compressed with E-PURE. Runs with the free
[`epure-runtime`](https://github.com/ExeaonLM/epure-runtime), and **stays
compressed in memory** -- the dense weight is never assembled.

| | |
|---|---|
| Base model | [Qwen/Qwen3-14B](https://huggingface.co/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

```python
pip install epure-runtime
```

```python
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.