---
library_name: transformers
tags:
- pruna-ai
- safetensors
---

# 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_pretrained(
    "PrunaAI/test-save-tiny-random-llama4-smashed"
)
# we can then run inference using the methods supported by the base model
```


For inference, you can use the inference methods of the original model like shown in [the original model card](https://huggingface.co/hf-internal-testing/tiny-random-llama4?library=transformers).
 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": null,
    "factorizer": null,
    "kernel": null,
    "pruner": null,
    "quantizer": null,
    "batch_size": 1,
    "device": "cpu",
    "device_map": null,
    "save_fns": [],
    "load_fns": [
        "transformers"
    ],
    "reapply_after_load": {
        "factorizer": null,
        "pruner": null,
        "quantizer": null,
        "kernel": null,
        "cacher": null,
        "compiler": null,
        "batcher": null
    }
}
```

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