---
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](https://blog.vessl.ai/ai-infrastructure-llm).
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
```
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
```
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
```
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://
```
## 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](https://github.com/vessl-ai/hub-model/blob/a74e87564d0775482fe6c56ff811bd8a9821f809/llama2-finetuning/finetuning.py#L97-L109).
```python
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](https://vessl.ai), 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.