This repository contains an LLM Finetuning template from which a simple ZenML project can be generated. It contains a collection of steps, pipelines, configurations and other artifacts and useful resources that can get you started with finetuning open-source LLMs with ZenML.
🔥 Do you have a personal project powered by ZenML that you would like to see here?
At ZenML, we are looking for design partnerships and collaboration to help us better understand the real-world scenarios in which MLOps is being used and to build the best possible experience for our users. If you are interested in sharing all or parts of your project with us in the form of a ZenML project template, please join our Slack and leave us a message!
| Parameter | Description | Default |
|---|---|---|
| Name | The name of the person/entity holding the copyright | ZenML GmbH |
| The email of the person/entity holding the copyright | [email protected] | |
| Project Name | Short name for your project | ZenML LLM Finetuning project |
| Project Version | The version of your project | 0.1.0 |
| Project License | The license under which your project will be released (one of Apache Software License 2.0, MIT license, BSD license, ISC license, GNU General Public License v3 and Not open source) |
Apache Software License 2.0 |
| Product name | The technical name to prefix all tech assets (pipelines, models, etc.) | llm_lora |
| Model repository | The Huggingface repository of the model to finetune | mistralai/Mistral-7B-Instruct-v0.1 |
| From safetensors | Whether the Huggingface model repository stores the model weights as safetensors | False |
| CUDA version | The available cuda version installed in the remote orchestrator environment (one of CUDA 11.8 and CUDA 12.1) |
CUDA 11.8 |
| Merged model repository | The huggingface repository to which the finetuned model should be pushed | - |
| Adapter repository | The huggingface repository to which the finetuned adapter should be pushed | - |
| Remote ZenML Server URL | Optional URL of a remote ZenML server for support scripts | - |
First, to use the templates, you need to have Zenml and its templates extras installed:
pip install "zenml[templates]"Now you can generate a project from one of the existing templates by using the --template flag with the zenml init command:
zenml init --template <short_name_of_template>
# example: zenml init --template llm_finetuningRunning the command above will result in input prompts being shown to you. If you would like to rely on default values for the ZenML project template - you can add --template-with-defaults to the same command, like this:
zenml init --template <short_name_of_template> --template-with-defaults
# example: zenml init --template llm_finetuning --template-with-defaults