# Pre-training
We use [nanotron](https://github.com/huggingface/nanotron/) library for training SmolLM and SmolLM2 base models.

The scripts for training SmolLM v1 can be found in the `smollm1` folder. SmolLM2 has a similar architecture and setup but uses an improved data mixture that we curated and significantly longer training periods (11 trillion tokens for the 1.7B, 4 trillion for the 360M and 2 trillion for the 135M). We will upload the SmolLM2 configs soon.

## Setup

Please refer to [nanotron](https://github.com/huggingface/nanotron/) for detailed instructions on setting up your training environment and launching jobs.

After setting up the environment and tokenizing the training datasets with [datatrove](https://github.com/huggingface/datatrove) (instructions available [here](https://github.com/huggingface/nanotron/blob/main/docs/nanoset.md#nanosets)), you can modify the configurations to match your number of nodes and local paths.

Below is an example of launching SmolLM1 135M training on 1 node (you can change the DP value to 8 in the config and adjust the batch size) and run:

```bash
git clone https://github.com/huggingface/nanotron
cd nanotron
# follow installation
CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node=8 run_train.py --config-file smollm1/config_smollm1_135M.yaml
```

If you are working on a slurm cluster, you can modify the `launch.slurm` and launch the training with:

```bash
sbatch launch.slurm
```
> [!NOTE]
> Don't forget to create the logs directory before launching the job: