h2oai / h2o-llmstudio

Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). Jump to• With H2O LLM Studio, you can• Quickstart• What's New• Setup• Recommended Install• Virtual Environments• Run H2O LLM Studio GUI• Run H2O LLM Studio GUI using Docker• Run H2O LLM Studio with command line interface (CLI)• Troubleshooting• Data format and example data• Training your model• Example: Run on OASST data via CLI• Model checkpoints• Documentation• Contributing• License With H2O LLM Studio, you can• easily and effectively fine-tune LLMs **without the need for any coding experience**.• use a **graphical user interface (GUI)** specially designed for large language models.• fine-tune any LLM using a large variety of hyperparameters.• use recent fine-tuning techniques such as Low-Rank Adaptation (LoRA) and 8-bit model training with a low memory footprint.• use Reinforcement Learning (RL) to fine-tune your model (experimental).• use advanced evaluation metrics to judge generated answers by the model.• track and compare your model performance visually. In addition, Neptune and W&B integration can be used.• chat with your model and get instant feedback on your model performance.• easily export your model to the Hugging Face Hub and share it with the community. Quickstart For questions, discussing, or just hanging out, come and join our Discord! Use cloud-based runpod.io instance to run the latest version of H2O LLM Studio with GUI. Using CLI for fine-tuning LLMs: What's New• PR 788 New problem type for Causal Regression Modeling allows to train single target regression data using LLMs.• PR 747 Fully removed RLHF in favor of DPO/IPO/KTO optimization.• PR 741 Removing separate max length settings for prompt and answer in favor of a single settings better resembling functionality from .• PR 592 Added for DPO modeling allowing to train models with simple preference data. Data currently needs to be manually prepared by randomly matching positive and negative examples as pairs.• PR 592 Starting to deprecate RLHF in favor of DPO/IPO optimization. Training is disabled, but old experiments are still viewable. RLHF will be fully removed in a future release.• PR 530 Introduced a new problem type for DPO/IPO optimization. This optimization technique can be used as an alternative to RLHF.• PR 288 Introduced DeepSpeed for sharded training allowing to train larger models on machines with multiple GPUs. Requires NVLink. This feature replaces FSDP and offers more flexibility. DeepSpeed requires a system installation of CUDA Toolkit and we recommend using version 12.1. See Recommended Install.• PR 449 New problem type for Causal Classification Modeling allows to train binary and multiclass models using LLMs.• PR 364 User secrets are now handled more securely and flexible. Support for handling secrets using the 'keyring' library was added. User settings are tried to be migrated automatically. Please note that due to current rapid development we cannot guarantee full backwards compatibility of new functionality. We thus recommend to pin the version of the framework to the one you used for your experiments. For resetting, please delete/backup your and folders. Setup H2O LLM Studio requires a machine with Ubuntu 16.04+ and at least one recent NVIDIA GPU with NVIDIA drivers version >= 470.57.02. For larger models, we recommend at least 24GB of GPU memory. For more information about installation prerequisites, see the Set up H2O LLM Studio guide in the documentation. For a performance comparison of different GPUs, see the H2O LLM Studio performance guide in the documentation. Recommended Install The recommended way to install H2O LLM Studio is using with Python 3.10. To install Python 3.10 on Ubuntu 20.04+, execute the following commands: Installing NVIDIA Drivers (if required) If deploying on a 'bare metal' machine running Ubuntu, one may need to install the required NVIDIA drivers and CUDA. The following commands show how to retrieve the latest drivers for a machine running Ubuntu 20.04 as an example. One can update the following based on their OS. Virtual environments We offer various ways of setting up the necessary python environment. UV virtual environment The following command will create a virtual environment using and will install the dependencies: Using requirements.txt If you wish to use another virtual environment, you can also install the dependencies using the requirements.txt file: Run H2O LLM Studio GUI You can start H2O LLM Studio using the following command: This command will start the H2O Wave server and app. Navigate to (we recommend using Chrome) to access H2O LLM Studio and start fine-tuning your models! If you are running H2O LLM Studio with a custom environment other than , you need to start the app as follows: Run H2O LLM Studio GUI using Docker Install Docker first by following instructions from NVIDIA Containers. Make sure to have installed on your machine as outlined in the instructions. H2O LLM Studio images are stored in the h2oai Docker Hub container repository. Navigate to (we recommend using Chrome) to access H2O LLM Studio and start fine-tuning your models! (Note other helpful docker commands are and .) Run H2O LLM Studio with command line interface (CLI) You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration .yaml file that contains all the experiment parameters. To fine-tune using H2O LLM Studio with CLI use the following command: To run on multiple GPUs in DDP mode, run the following command: By default, the framework will run on the first GPUs. If you want to specify specific GPUs to run on, use the environment variable before the command. To start an interactive chat with your trained model, use the following command: where is the output folder of the experiment you want to chat with (see configuration). The interactive chat will also work with model that wer…