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# Installation

Transformers works with [PyTorch](https://pytorch.org/get-started/locally/). It has been tested on Python 3.9+ and PyTorch 2.2+.

## Virtual environment

[uv](https://docs.astral.sh/uv/) is an extremely fast Rust-based Python package and project manager and requires a [virtual environment](https://docs.astral.sh/uv/pip/environments/) by default to manage different projects and avoids compatibility issues between dependencies.

It can be used as a drop-in replacement for [pip](https://pip.pypa.io/en/stable/), but if you prefer to use pip, remove `uv` from the commands below.

> [!TIP]
> Refer to the uv [installation](https://docs.astral.sh/uv/guides/install-python/) docs to install uv.

Create a virtual environment to install Transformers in.

```bash
uv venv .env
source .env/bin/activate
```

## Python

Install Transformers with the following command.

[uv](https://docs.astral.sh/uv/) is a fast Rust-based Python package and project manager.

```bash
uv pip install transformers
```

For GPU acceleration, install the appropriate CUDA drivers for [PyTorch](https://pytorch.org/get-started/locally).

Run the command below to check if your system detects an NVIDIA GPU.

```bash
nvidia-smi
```

To install a CPU-only version of Transformers, run the following command.

```bash
uv pip install torch --index-url https://download.pytorch.org/whl/cpu
uv pip install transformers
```

Test whether the install was successful with the following command. It should return a label and score for the provided text.

```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('hugging face is the best'))"
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```

### Source install

Installing from source installs the *latest* version rather than the *stable* version of the library. It ensures you have the most up-to-date changes in Transformers and it's useful for experimenting with the latest features or fixing a bug that hasn't been officially released in the stable version yet.

The downside is that the latest version may not always be stable. If you encounter any problems, please open a [GitHub Issue](https://github.com/huggingface/transformers/issues) so we can fix it as soon as possible.

Install from source with the following command.

```bash
uv pip install git+https://github.com/huggingface/transformers
```

Check if the install was successful with the command below. It should return a label and score for the provided text.

```bash
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('hugging face is the best'))"
[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
```

### Editable install

An [editable install](https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs) is useful if you're developing locally with Transformers. It links your local copy of Transformers to the Transformers [repository](https://github.com/huggingface/transformers) instead of copying the files. The files are added to Python's import path.

```bash
git clone https://github.com/huggingface/transformers.git
cd transformers
uv pip install -e .
```

> [!WARNING]
> You must keep the local Transformers folder to keep using it.

Update your local version of Transformers with the latest changes in the main repository with the following command.

```bash
cd ~/transformers/
git pull
```

## conda

[conda](https://docs.conda.io/projects/conda/en/stable/#) is a language-agnostic package manager. Install Transformers from the [conda-forge](https://anaconda.org/conda-forge/transformers) channel in your newly created virtual environment.

```bash
conda install conda-forge::transformers
```

## Set up

After installation, you can configure the Transformers cache location or set up the library for offline usage.

### Cache directory

When you load a pretrained model with [`~PreTrainedModel.from_pretrained`], the model is downloaded from the Hub and locally cached.

Every time you load a model, it checks whether the cached model is up-to-date. If it's the same, then the local model is loaded. If it's not the same, the newer model is downloaded and cached.

The default directory given by the shell environment variable `TRANSFORMERS_CACHE` is `~/.cache/huggingface/hub`. On Windows, the default directory is `C:\Users\username\.cache\huggingface\hub`.

Cache a model in a different directory by changing the path in the following shell environment variables (listed by priority).

1. [HF_HUB_CACHE](https://hf.co/docs/huggingface_hub/package_reference/environment_variables#hfhubcache) or `TRANSFORMERS_CACHE` (default)
2. [HF_HOME](https://hf.co/docs/huggingface_hub/package_reference/environment_variables#hfhome)
3. [XDG_CACHE_HOME](https://hf.co/docs/huggingface_hub/package_reference/environment_variables#xdgcachehome) + `/huggingface` (only if `HF_HOME` is not set)

Older versions of Transformers uses the shell environment variables `PYTORCH_TRANSFORMERS_CACHE` or `PYTORCH_PRETRAINED_BERT_CACHE`. You should keep these unless you specify the newer shell environment variable `TRANSFORMERS_CACHE`.

### Offline mode

To use Transformers in an offline or firewalled environment requires the downloaded and cached files ahead of time. Download a model repository from the Hub with the [`~huggingface_hub.snapshot_download`] method.

> [!TIP]
> Refer to the [Download files from the Hub](https://hf.co/docs/huggingface_hub/guides/download) guide for more options for downloading files from the Hub. You can download files from specific revisions, download from the CLI, and even filter which files to download from a repository.

```py
from huggingface_hub import snapshot_download

snapshot_download(repo_id="meta-llama/Llama-2-7b-hf", repo_type="model")
```

Set the environment variable `HF_HUB_OFFLINE=1` to prevent HTTP calls to the Hub when loading a model.

```bash
HF_HUB_OFFLINE=1 \
python examples/pytorch/language-modeling/run_clm.py --model_name_or_path meta-llama/Llama-2-7b-hf --dataset_name wikitext ...
```

Another option for only loading cached files is to set `local_files_only=True` in [`~PreTrainedModel.from_pretrained`].

```py
from transformers import LlamaForCausalLM

model = LlamaForCausalLM.from_pretrained("./path/to/local/directory", local_files_only=True)
```
