---
language:
- en
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
tags:
- pruna-ai
- safetensors
extra_gated_prompt: By clicking "Agree", you agree to the [FluxDev Non-Commercial
  License Agreement](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
  and acknowledge the [Acceptable Use Policy](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/POLICY.md).
---

# Model Card for LenSch/torchao

This model was created using the [pruna](https://github.com/PrunaAI/pruna) library. Pruna is a model optimization framework built for developers, enabling you to deliver more efficient models with minimal implementation overhead.

## Usage

First things first, you need to install the pruna library:

```bash
pip install pruna
```

You can [use the library_name library to load the model](https://huggingface.co/LenSch/torchao?library=library_name) but this might not include all optimizations by default.

To ensure that all optimizations are applied, use the pruna library to load the model using the following code:

```python
from pruna import PrunaModel

loaded_model = PrunaModel.from_pretrained(
    "LenSch/torchao"
)
# we can then run inference using the methods supported by the base model
```

 Alternatively, you can visit [the Pruna documentation](https://docs.pruna.ai/en/stable/) for more information.

## Smash Configuration

The compression configuration of the model is stored in the `smash_config.json` file, which describes the optimization methods that were applied to the model.

```bash
{
    "batcher": null,
    "cacher": null,
    "compiler": "torch_compile",
    "factorizer": null,
    "kernel": "flash_attn3",
    "pruner": null,
    "quantizer": "torchao",
    "torch_compile_backend": "inductor",
    "torch_compile_dynamic": null,
    "torch_compile_fullgraph": false,
    "torch_compile_make_portable": false,
    "torch_compile_max_kv_cache_size": 400,
    "torch_compile_mode": "max-autotune-no-cudagraphs",
    "torch_compile_seqlen_manual_cuda_graph": 100,
    "torch_compile_target": "model",
    "torchao_excluded_modules": "none",
    "torchao_quant_type": "int8dq",
    "batch_size": 1,
    "device": "cuda:0",
    "device_map": null,
    "save_fns": [
        "save_before_apply",
        "save_before_apply"
    ],
    "load_fns": [
        "diffusers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": "torchao",
        "kernel": "flash_attn3",
        "cacher": null,
        "compiler": "torch_compile",
        "batcher": null
    }
}
```

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