caching_only / README.md
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---
language:
- en
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
tags:
- safetensors
- pruna-ai
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/caching_only
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/caching_only?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/caching_only"
)
# 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": "fora",
"compiler": "stable_fast",
"factorizer": null,
"kernel": null,
"pruner": null,
"quantizer": null,
"fora_interval": 4,
"fora_start_step": 1,
"batch_size": 1,
"device": "cuda:0",
"device_map": null,
"save_fns": [
"save_before_apply"
],
"load_fns": [
"diffusers"
],
"reapply_after_load": {
"factorizer": null,
"pruner": null,
"quantizer": null,
"kernel": null,
"cacher": "fora",
"compiler": "stable_fast",
"batcher": null
}
}
```
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