# TorchAO

TorchAO is an architecture optimization library for PyTorch, it provides high performance dtypes, optimization techniques and kernels for inference and training, featuring composability with native PyTorch features like torch.compile, FSDP etc.. Some benchmark numbers can be found [here](https://github.com/pytorch/ao/tree/main/torchao/quantization#benchmarks).

We recommend installing the latest torchao nightly with

```console
# Install the latest TorchAO nightly build
# Choose the CUDA version that matches your system (cu126, cu128, etc.)
pip install --pre torchao>=10.0.0 --index-url https://download.pytorch.org/whl/nightly/cu126
```

## Quantizing HuggingFace Models
You can quantize your own huggingface model with torchao, e.g. [transformers](https://huggingface.co/docs/transformers/main/en/quantization/torchao) and [diffusers](https://huggingface.co/docs/diffusers/en/quantization/torchao), and save the checkpoint to huggingface hub like [this](https://huggingface.co/jerryzh168/llama3-8b-int8wo) with the following example code:

```Python
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8WeightOnlyConfig

model_name = "meta-llama/Meta-Llama-3-8B"
quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
quantized_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

hub_repo = # YOUR HUB REPO ID
tokenizer.push_to_hub(hub_repo)
quantized_model.push_to_hub(hub_repo, safe_serialization=False)
```

Alternatively, you can use the TorchAO Quantization space for quantizing models with a simple UI.
See: https://huggingface.co/spaces/medmekk/TorchAO_Quantization
