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Overview

This tutorial introduces you to Cerebras essentials, including data preprocessing, training scripts, configuration files, and checkpoint conversion tools. You’ll learn these concepts by pretraining Meta’s Llama 3 8B on 40,000 lines of Shakespeare.

In this quickstart, you will:

  • Set up your environment
  • Preprocess a small dataset
  • Pretrain and evaluate a model
  • Convert your model checkpoint for Hugging Face

In this tutorial, you will train your model for a short while on a small dataset. A high quality model requires a longer training run, as well as a much larger dataset.

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Prerequisites

To begin this guide, you must have:

  • Cerebras system access. If you don’t have access, contact Cerebras Support.
  • Completed setup and installation.

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Workflow

1

Create Model Directory & Copy Configs

First, save the working directory to an environment variable:

export MODELZOO_PARENT=$(pwd)

Then, create a dedicated folder to store assets (like data and model configs) and generated files (such as processed datasets, checkpoints, and logs):

mkdir pretraining_tutorial

Next, copy the sample configs into your folder. These include model configs, evaluation configs, and data configs.

cp modelzoo/src/cerebras/modelzoo/tutorials/pretraining/* pretraining_tutorial

We use cp here to copy configs specifically designed for this tutorial. For general use with Model Zoo models, we recommend using cszoo config pull. See the CLI command reference for details.

2

Inspect Configs

Before moving on, inspect the configuration files you just copied to confirm that the parameters are set as expected.

3

Preprocess Data

Use your data configs to preprocess your “train” and “validation” datasets:

cszoo data_preprocess run --config pretraining_tutorial/train_data_config.yaml
cszoo data_preprocess run --config pretraining_tutorial/valid_data_config.yaml

You should then see your preprocessed data in pretraining_tutorial/train_data/ and pretraining_tutorial/valid_data/ (see the output_dir parameter in your data configs).

When using the Hugging Face CLI to download a dataset, you may encounter the following error: KeyError: 'tags'

This issue occurs due to an outdated version of the huggingface_hub package. To resolve it, update the package by running:

pip install --upgrade huggingface_hub==0.26.1

An example of a “train” dataset looks as follows:

{
    "text": "First Citizen:\nBefore we proceed any further, hear me "
}

If you are interested, you can read more about the various parameters and pre-built utilities for preprocessing common data formats. You can also follow end-to-end tutorials for various use cases such as instruction fine-tuning and extending context lengths using position interpolation.

4

Train and Evaluate Model

Update train_dataloader.data_dir and val_dataloader.data_dir in your model config to use the absolute paths of your preprocessed data:

sed -i "s|data_dir: train_data|data_dir: ${MODELZOO_PARENT}/pretraining_tutorial/train_data|" \
pretraining_tutorial/model_config.yaml

sed -i "s|data_dir: valid_data|data_dir: ${MODELZOO_PARENT}/pretraining_tutorial/valid_data|" \
pretraining_tutorial/model_config.yaml

Now you’re ready to launch training. Use the cszoo fit command to submit a job, passing in your updated model config. This command automatically uses the locations and packages defined in your config. Click here for more information.

cszoo fit pretraining_tutorial/model_config.yaml --mgmt_namespace <namespace>

You should then see something like this in your terminal:

Transferring weights to server: 100%|██| 1165/1165 [01:00<00:00, 19.33tensors/s]
INFO:   Finished sending initial weights
INFO:   | Train Device=CSX, Step=50, Loss=8.31250, Rate=69.37 samples/sec, GlobalRate=69.37 samples/sec
INFO:   | Train Device=CSX, Step=100, Loss=7.25000, Rate=68.41 samples/sec, GlobalRate=68.56 samples/sec
...

Once training is complete, you will find several artifacts in the pretraining_tutorial/model folder (see the model_dir parameter in your model config). These include:

  • Checkpoints
  • TensorBoard event files
  • Run logs
  • A copy of the model config

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Inspect Training Logs

Monitor your training during the run or visualize TensorBoard event files afterwards:

tensorboard --bind_all --logdir="pretraining_tutorial/model"
5

Port Model to Hugging Face

Once you train (and evaluate) your model, you can port it to Hugging Face to generate outputs:

cszoo checkpoint convert --model llama --src-fmt cs-auto --tgt-fmt hf --config pretraining_tutorial/model_config.yaml --output-dir pretraining_tutorial/to_hf pretraining_tutorial/model/checkpoint_0.mdl

This will create both Hugging Face config files and a converted checkpoint under pretraining_tutorial/to_hf.

6

Validate Checkpoint and Configs

You can now generate outputs using Hugging Face:

pip install 'transformers\[torch\]'

python

Python 3.8.16 (default, Mar 18 2024, 18:27:40)    
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

>>> from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig

>>> from transformers import pipeline

>>> tokenizer = AutoTokenizer.from_pretrained("baseten/Meta-Llama-3-tokenizer")

>>> config = AutoConfig.from_pretrained("pretraining_tutorial/to_hf/model_config_to_hf.json")

>>> model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="pretraining_tutorial/to_hf/checkpoint_0_to_hf.bin", config = config)

>>> text = "Generative AI is "

>>> pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

>>> generated_text = pipe(text, max_length=50, do_sample=False, no_repeat_ngram_size=2, eos_token_id=pipeline.tokenizer.eos_token_id, pad_token_id=pipeline.tokenizer.eos_token_id)[0]

>>> print(generated_text['generated_text'])

>>> exit()

As a reminder, in this quickstart, you did not train your model for very long. A high quality model requires a longer training run, as well as a much larger dataset.

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Conclusion

Congratulations! In this tutorial, you followed an end-to-end workflow to pretrain a model on a Cerebras system and learn about essential tools and scripts.

As part of this, your learned how to:

  • Setup your environment
  • Preprocess a small dataset
  • Pretrain and evaluate a model
  • Port your model to Hugging Face

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What’s Next?