File size: 2,736 Bytes
377718f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | ---
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.
|