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---
title: Llama2-7B Fine-tuning
description: Fine-tune Llama2-7B with a code instructions dataset
version: EN
---
## Try out this model on [VESSL Hub](https://vessl.ai/hub).
This example fine-tunes [Llama 2](https://ai.meta.com/llama/) on a code instruction dataset. The code instruction dataset is consisted of 1.6K samples and follows the format of Stanford's [Alpaca dataset](https://github.com/gururise/AlpacaDataCleaned).
To optimize the training process into a single GPU with moderate memory, the model uses [8 bit quantization](https://huggingface.co/blog/hf-bitsandbytes-integration) and LoRA (Low-Rank Adaptation).
In the code we are referencing under `/code/`, we added our Python SDK for logging key metrics like loss and learning rate. You can check these values in real-time under Plots. The run completes by uploading the model checkpoint to VESSL AI model registry, as defined under `export`.
<img
className="rounded-md"
src="/images/llama2-metrics.png"
/>
<img
className="rounded-md"
src="/images/llama2-uploaded-model.png"
/>
## Running the model
You can run the model with our quick command.
```sh
vessl run create -f llama2_fine-tuning.yaml
```
Here's a rundown of the `llama2_fine-tuning.yaml` file.
```yaml
name: llama2-finetuning
description: finetune llama2 with code instruction alpaca dataset
resources:
cluster: vessl-gcp-oregon
preset: v1.l4-1.mem-27
image: quay.io/vessl-ai/hub:torch2.1.0-cuda12.2-202312070053
import:
/model/: vessl-model://vessl-ai/llama2/1
/code/:
git:
url: https://github.com/vessl-ai/hub-model
ref: main
/dataset/: vessl-dataset://vessl-ai/code_instructions_small_alpaca
export:
/trained_model/: vessl-model://vessl-ai/llama2-finetuned
/artifacts/: vessl-artifact://
run:
- command: |-
pip install -r requirements.txt
mkdir /model_
cd /model
mv llama_2_7b_hf.zip /model_
cd /model_
unzip llama_2_7b_hf.zip
cd /code/llama2-finetuning
python finetuning.py
workdir: /code/llama2-finetuning
``` |