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--- |
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title: Llama2-7B Fine-tuning |
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description: Fine-tune Llama2-7B with a code instructions dataset |
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version: EN |
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--- |
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## Try out this model on [VESSL Hub](https://vessl.ai/hub). |
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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). |
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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). |
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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`. |
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<img |
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className="rounded-md" |
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src="/images/llama2-metrics.png" |
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/> |
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<img |
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className="rounded-md" |
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src="/images/llama2-uploaded-model.png" |
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/> |
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## Running the model |
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You can run the model with our quick command. |
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```sh |
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vessl run create -f llama2_fine-tuning.yaml |
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``` |
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Here's a rundown of the `llama2_fine-tuning.yaml` file. |
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```yaml |
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name: llama2-finetuning |
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description: finetune llama2 with code instruction alpaca dataset |
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resources: |
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cluster: vessl-gcp-oregon |
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preset: v1.l4-1.mem-27 |
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image: quay.io/vessl-ai/hub:torch2.1.0-cuda12.2-202312070053 |
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import: |
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/model/: vessl-model://vessl-ai/llama2/1 |
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/code/: |
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git: |
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url: https://github.com/vessl-ai/hub-model |
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ref: main |
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/dataset/: vessl-dataset://vessl-ai/code_instructions_small_alpaca |
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export: |
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/trained_model/: vessl-model://vessl-ai/llama2-finetuned |
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/artifacts/: vessl-artifact:// |
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run: |
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- command: |- |
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pip install -r requirements.txt |
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mkdir /model_ |
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cd /model |
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mv llama_2_7b_hf.zip /model_ |
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cd /model_ |
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unzip llama_2_7b_hf.zip |
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cd /code/llama2-finetuning |
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python finetuning.py |
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workdir: /code/llama2-finetuning |
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``` |
