---
license: apache-2.0
base_model: Qwen/Qwen3-32B
base_model_relation: quantized
library_name: epure-runtime
pipeline_tag: text-generation
language:
  - en
tags:
  - exeaon
  - epure
  - compressed
  - quantized
---

# Exeaon1-Kese-32B

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.

> **Requires `epure-runtime>=0.2.4`.** Earlier versions load every model as
> float16. Qwen3-32B is trained in bfloat16 and its activations exceed the
> float16 range, so on 0.2.3 this model overflows to infinity partway through
> the second layer and produces garbage on prompts of ordinary length. This is
> not a soft warning: the failure is silent on short prompts and total on real
> ones.
>
> ```
> pip install "epure-runtime>=0.2.4"
> ```

| | |
|---|---|
| Base model | [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) |
| Size on disk | **16.42 GB** |
| Compression | **3.99×** |
| 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 | Qwen3-32B | Exeaon1-Kese-32B | delta |
|---|---|---|---|
| ARC-Challenge | 57.33 | 57.00 | -0.33 |
| ARC-Easy | 82.67 | 82.67 | +0.00 |
| HellaSwag | 69.33 | 69.67 | +0.34 |
| PIQA | 82.33 | 83.00 | +0.67 |
| **mean** | **72.91** | **73.09** | **+0.17** |

**Retention: 100.2%** of base mean accuracy.

The compressed model scores fractionally above the original. That is not a
gain — it is measurement noise at `limit=300`, where one 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.

## Usage

```python
pip install "epure-runtime>=0.2.4"
```

```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]))
```

## Notes on speed

On a large GPU this runs slower than dense fp16. A vendor tensor-core GEMM is
heavily tuned and a large card 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.

Qwen3-32B needs roughly 61 GB in bfloat16. This container needs 16.42 GB on
disk and stays packed in memory.

## Licence

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