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test-save-tiny-random-llama4-smashed

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test-save-tiny-random-llama4-smashed
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---
library_name: transformers
tags:
- pruna-ai
---

# Model Card for PrunaAI/test-save-tiny-random-llama4-smashed

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 transformers library to load the model](https://huggingface.co/PrunaAI/test-save-tiny-random-llama4-smashed?library=transformers) 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_hub(
    "PrunaAI/test-save-tiny-random-llama4-smashed"
)
```

After loading the model, you can use the inference methods of the original model. Take a look at the [documentation](https://pruna.readthedocs.io/en/latest/index.html) for more usage 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": null,
    "distiller": null,
    "enhancer": null,
    "factorizer": null,
    "pruner": null,
    "quantizer": null,
    "recoverer": null,
    "batch_size": 1,
    "device": "cpu",
    "save_fns": [],
    "load_fns": [
        "transformers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": null,
        "distiller": null,
        "cacher": null,
        "recoverer": null,
        "compiler": null,
        "batcher": null,
        "enhancer": null
    }
}
```

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