| # TPU support | |
| Lit-LLaMA used `lightning.Fabric` under the hood, which itself supports TPUs (via [PyTorch XLA](https://github.com/pytorch/xla)). | |
| The following commands will allow you to set up a `Google Cloud` instance with a [TPU v4](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm) VM: | |
| ```shell | |
| gcloud compute tpus tpu-vm create lit-llama --version=tpu-vm-v4-pt-2.0 --accelerator-type=v4-8 --zone=us-central2-b | |
| gcloud compute tpus tpu-vm ssh lit-llama --zone=us-central2-b | |
| ``` | |
| Now that you are in the machine, let's clone the repository and install the dependencies | |
| ```shell | |
| git clone https://github.com/Lightning-AI/lit-llama | |
| cd lit-llama | |
| pip install -r requirements.txt | |
| ``` | |
| By default, computations will run using the new (and experimental) PjRT runtime. Still, it's recommended that you set the following environment variables | |
| ```shell | |
| export PJRT_DEVICE=TPU | |
| export ALLOW_MULTIPLE_LIBTPU_LOAD=1 | |
| ``` | |
| > **Note** | |
| > You can find an extensive guide on how to get set-up and all the available options [here](https://cloud.google.com/tpu/docs/v4-users-guide). | |
| Since you created a new machine, you'll probably need to download the weights. You could scp them into the machine with `gcloud compute tpus tpu-vm scp` or you can follow the steps described in our [downloading guide](download_weights.md). | |
| ## Inference | |
| Generation works out-of-the-box with TPUs: | |
| ```shell | |
| python3 generate.py --prompt "Hello, my name is" --num_samples 2 | |
| ``` | |
| This command will take a long time as XLA needs to compile the graph (~13 min) before running the model. | |
| In fact, you'll notice that the second sample takes considerable less time (~12 sec). | |
| ## Finetuning | |
| Coming soon. | |
| > **Warning** | |
| > When you are done, remember to delete your instance | |
| > ```shell | |
| > gcloud compute tpus tpu-vm delete lit-llama --zone=us-central2-b | |
| > ``` |