---
base_model: Tongyi-MAI/Z-Image-Turbo
library_name: diffusers
tags:
- matmultoken
- pouw
- proof-of-useful-work
- inference-mining
---

# Z-Image-Turbo-pouw

A **self-contained pouw model**, based on [`Tongyi-MAI/Z-Image-Turbo`]( https://huggingface.co/Tongyi-MAI/Z-Image-Turbo ). It **bundles the full base weights** (apache-2.0) together with the metadata that makes it mine MatMulToken Proof-of-Useful-Work *while it serves* — pull this one repo and it runs, no second download.

MatMulToken's mining is **output-preserving**: generation is bit-identical to the base model. The
eligible transformer matmuls (`in_features == common_dim = 3840`) are reused as PoW
lottery tickets — you serve real images **and** mine on the same compute, no second matmul.

It is **GPU-agnostic** (portable Triton/PyTorch kernels, no CUDA build): RTX 3090 (sm86) →
5090 → H100 → B200, same code.

## Mining shape

| field | value |
|---|---|
| base model | `Tongyi-MAI/Z-Image-Turbo` |
| modality | image |
| common_dim | 3840 |
| rank | 32 |
| mine_layers | 16 (overhead dial; layer count) |
| pipeline | diffusers |

## Use

```python
# install the MatMulToken miner into your serving venv (see the MatMulToken repo)
#   uv pip install --no-deps <matmul_mining wheel> -e miner-base -e vllm-matmul ...
from vllm_matmul import matmultoken_load
b = matmultoken_load("Matmultoken/Z-Image-Turbo-pouw", gateway=False)   # gateway=True for the live chain
b["pipe"]("a single matmul on a clean white desk, studio light")   # serves AND mines
print("wrapped", b["wrapped"], "mining linears; common_dim", b["common_dim"])
```

`gateway=False` attaches an idle local job (for testing the mining path); `gateway=True`
connects to a running MatMulToken gateway for the live block template / target.

## Notes

- The live PoW **job + difficulty target** always come from the chain at runtime — never baked
  into this repo. GPU kernels compile per-arch on first run (one-time, cached on disk).
- Published under the **`Matmultoken`** organization. The base weights (apache-2.0) are bundled in this repo at a pinned snapshot for a reproducible mining shape; the original model's LICENSE and attribution are preserved in-repo.

*Generated by MatMulToken `publish_pouw_models.py`. License: MIT.*