| --- |
| 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.* |
|
|
