guides/get-started/llama2.md · vessl/vessl-docs at main

metadata

title: Llama 2 Fine-tuning
description: Fine-tune Llama2-7B with instruction datasets
icon: circle-3
version: EN

This example fine-tunes Llama2-7B with a code instruction dataset, illustrating how VESSL AI offloads the infrastructural challenges of large-scale AI workloads and help you train multi-billion-parameter models in hours, not weeks.

This is the most compute-intensive workload yet but you will see how VESSL AI's efficient training stack enables you to seamlessly scale and execute multi-node training. For a more in-depth guide, refer to our blog post.

Try out the Quickstart example with a single click on VESSL Hub. See the completed YAML file and final code for this example.

What you will do

Fine-tune an LLM with zero-to-minimum setup
Mount a custom dataset
Store and export model artifacts

Writing the YAML

Let's fill in the llama2_fine-tuning.yml file.

Let's set spin up an instance. Nothing new here.

    ```yaml
name: Llama2-7B fine-tuning 
description: Fine-tune Llama2-7B with instruction datasets
resources:
    cluster: vessl-gcp-oregon
    preset: gpu-l4-small
image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
    ```
</Step>

<Step title="Mount the code, modal, and dataset">
Here, in addition to our GitHub repo and Hugging Face model, we are also mounting a Hugging Face dataset. 

As with our HF model, mountint data is as simple as referencing the URL beginnging with the `hf://` scheme -- this goes the same for other cloud storages as well, `s3://` for Amazon S3 for example.	

```yaml
name: llama2-finetuning
description: Fine-tune Llama2-7B with instruction datasetst
resources:
    cluster: vessl-gcp-oregon
    preset: gpu-l4-small
image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
import:
    /model/: hf://huggingface.co/VESSL/llama2
    /code/:
        git:
            url: https://github.com/vessl-ai/hub-model
            ref: main

/dataset/: hf://huggingface.co/datasets/VESSL/code_instructions_small_alpaca ```

<Step title="Write the run commands">
Now that we have the three pillars of model development mounted on our remote workload, we are ready to define the run command. Let's install additiona Python dependencies and run `finetuning.py` -- which calls for our HF model and datasets in the `config.yaml` file. 

```yaml
name: llama2-finetuning
description: Fine-tune Llama2-7B with instruction datasetst
resources:
    cluster: vessl-gcp-oregon
    preset: gpu-l4-small
image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
import:
    /model/: hf://huggingface.co/VESSL/llama2
    /code/:
        git:
            url: https://github.com/vessl-ai/hub-model
            ref: main
    /dataset/: hf://huggingface.co/datasets/VESSL/code_instructions_small_alpaca
run:
    - command: |-
            pip install -r requirements.txt
            python finetuning.py
        workdir: /code/llama2-finetuning
```
</Step>

<Step title="Export a model artifact">
    You can keep track of model checkpoints by dedicating an `export` volume to the workload. After training is finished, trained models are uploaded to the `artifact` folder as model checkpoints.

    ```yaml
    name: llama2-finetuning
    description: Fine-tune Llama2-7B with instruction datasetst
    resources:
        cluster: vessl-gcp-oregon
        preset: gpu-l4-small
    image: quay.io/vessl-ai/torch:2.1.0-cuda12.2-r3
    import:
        /model/: hf://huggingface.co/VESSL/llama2
        /code/:
            git:
                url: https://github.com/vessl-ai/hub-model
                ref: main
        /dataset/: hf://huggingface.co/datasets/VESSL/code_instructions_small_alpaca
    run:
        - command: |-
                pip install -r requirements.txt
                python finetuning.py
            workdir: /code/llama2-finetuning
    export:
      /artifacts/: vessl-artifact://
```
</Step>

Running the workload

Once the workload is completed, you can follow the link in the terminal to get the output files including the model checkpoints under Files.

vessl run create -f llama2_fine-tuning.yml

Behind the scenes

With VESSL AI, you can launch a full-scale LLM fine-tuning workload on any cloud, at any scale, without worrying about these underlying system backends.

Model checkpointing — VESSL AI stores .pt files to mounted volumes or model registry and ensures seamless checkpointing of fine-tuning progress.
GPU failovers — VESSL AI can autonomously detect GPU failures, recover failed containers, and automatically re-assign workload to other GPUs.
Spot instances — Spot instance on VESSL AI works with model checkpointing and export volumes, saving and resuming the progress of interrupted workloads safely.
Distributed training — VESSL AI comes with native support for PyTorch DistributedDataParallel and simplifies the process for setting up multi-cluster, multi-node distributed training.
Autoscaling — As more GPUs are released from other tasks, you can dedicate more GPUs to fine-tuning workloads. You can do this on VESSL AI by adding the following to your existing fine-tuning YAML.

Tips & tricks

In addition to the model checkpoints, you can track key metrics and parameters with vessl.log Python SDK. Here's a snippet from finetuning.py.

class VesslLogCallback(TrainerCallback):
    def on_log(self, args, state, control, logs=None, **kwargs):
        if "eval_loss" in logs.keys():
            payload = {
                "eval_loss": logs["eval_loss"],
            }
            vessl.log(step=state.global_step, payload=payload)
        elif "loss" in logs.keys():
            payload = {
                "train_loss": logs["loss"],
                "learning_rate": logs["learning_rate"],
            }
            vessl.log(step=state.global_step, payload=payload)

Using our web interface

You can repeat the same process on the web. Head over to your Organization, select a project, and create a New run.

What's next?

We shared ho you can use VESSL AI to go from a simple Python container to a full-scale AI workload. We hope these guides give you a glimpse of what you can achieve with VESSL AI. For more resources, follow along our example models or use casese.

See VESSL AI in action with the latest open-source models and our example Runs. See the top use casese of VESSL AI from experiment tracking to cluster management.