tiny_llama_hqq / README.md
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---
library_name: transformers
tags:
- safetensors
- pruna_pro-ai
- pruna-ai
---
# Model Card for loulou2/tiny_llama_hqq
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_pro
```
You can [use the transformers library to load the model](https://huggingface.co/loulou2/tiny_llama_hqq?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_pro import PrunaProModel
loaded_model = PrunaProModel.from_pretrained(
"loulou2/tiny_llama_hqq"
)
# 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": null,
"distiller": null,
"distributer": null,
"enhancer": null,
"factorizer": null,
"kernel": null,
"pruner": null,
"quantizer": "hqq",
"recoverer": null,
"hqq_backend": "torchao_int4",
"hqq_compute_dtype": "torch.bfloat16",
"hqq_force_hf_implementation": true,
"hqq_group_size": 64,
"hqq_use_torchao_kernels": false,
"hqq_weight_bits": 4,
"batch_size": 1,
"device": "cuda",
"device_map": null,
"save_fns": [],
"load_fns": [
"transformers"
],
"reapply_after_load": {
"factorizer": null,
"pruner": null,
"quantizer": null,
"distiller": null,
"kernel": null,
"cacher": null,
"recoverer": null,
"distributer": null,
"compiler": null,
"batcher": null,
"enhancer": null
}
}
```
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