diff --git a/fine-tune.mdx b/fine-tune.mdx index 922cf1bf..e6e236ed 100644 --- a/fine-tune.mdx +++ b/fine-tune.mdx @@ -1,95 +1,94 @@ --- title: "Fine-tune a model" +description: "Learn how to fine-tune a large language model on Runpod using Axolotl." --- -This guide explains how to fine-tune a large language model using Runpod and Axolotl. You'll learn how to select a base model, configure your training environment, and start the fine-tuning process. +Fine-tuning is the process of taking a pre-trained large language model (LLM) and further training it on a smaller, specific dataset. This process adapts the model to a particular task or domain, improving its performance and accuracy for your use case. -## Prerequisites +This guide explains how to use Runpod's fine-tuning feature, powered by [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), to customize an LLM. You'll learn how to select a base model, choose a dataset, configure your training environment, and deploy your fine-tuned model. -Before you begin fine-tuning, ensure you have: +For more information about fine-tuning with Axolotl, see the [Axolotl Documentation](https://github.com/OpenAccess-AI-Collective/axolotl). -* A Runpod account with access to the Fine Tuning feature -* (Optional) A Hugging Face access token for gated models +## Requirements -## Select a base model +Before you begin, ensure you have: -To start fine-tuning, you'll need to choose a base model from Hugging Face: +* A Runpod account. +* (Optional) A [Hugging Face](https://huggingface.co/) account and an access token if you plan to use gated models or upload your fine-tuned model. -1. Navigate to the **Fine Tuning** section in the sidebar -2. Enter the Hugging Face model ID in the **Base Model** field +## Select a base model and dataset - * Example: `NousResearch/Meta-Llama-3-8B` +The base model is the starting point for your fine-tuning process, while the dataset provides the specific knowledge needed to adapt the base model to your task. -3. For gated models (requiring special access): +You can choose from thousands of models and datasets on [Hugging Face](https://huggingface.co/models). - 1. Generate a Hugging Face token with appropriate permissions - 2. Add your token in the designated field + +If you're experimenting with fine-tuning for the first time, we recommend trying the [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) model and the [tatsu-lab/alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset. + -## Select a dataset +## Deploy a fine-tuning Pod -You can choose a dataset from Hugging Face for fine-tuning: + + +Navigate to the [Fine-Tuning](https://console.runpod.io/fine-tuning) section in the Runpod console. + -1. Browse available datasets on [Hugging Face](https://huggingface.co/datasets?task_categories=task_categories:text-generation\&sort=trending) -2. Enter your chosen dataset identifier in the **Dataset** field - * Example: `tatsu-lab/alpaca` + +In the **Base Model** field, enter the Hugging Face model ID (e.g `NousResearch/Meta-Llama-3-8B`). In the **Dataset** field, enter the Hugging Face dataset ID (e.g `tatsu-lab/alpaca`). + -## Deploy the fine-tuning pod + +If you're using a gated model that requires special access, generate a Hugging Face token with the necessary permissions and add it to the **Hugging Face Access Token** field. + -Follow these steps to set up your training environment: + +Click **Deploy the Fine-Tuning Pod** to start configuring your fine-tuning Pod. + -1. Click **Deploy the Fine Tuning Pod** + +Select a GPU instance based on your model's requirements. Larger models and datasets require GPUs with more memory. + -2. Select a GPU instance based on your model's requirements: + +Finishing configuring the Pod, then click **Deploy on-demand**. This should open the detail pane for your Pod automatically. + - * Smaller models: Choose GPUs with less memory - * Larger models/datasets: Choose GPUs with higher memory capacity + +Click **Logs** to monitor the system logs for deployment progress. Wait for the success message: `"You've successfully configured your training environment!"` Depending on the size of your model and dataset, this may take some time. + -3. Monitor the system logs for deployment progress + +Once your training environment is ready, you can connect to it to configure and start the fine-tuning process. -4. Wait for the success message: `"You've successfully configured your training environment!"` +Click **Connect** and choose your preferred connection method: +* **Jupyter Notebook**: A browser-based notebook interface. +* **Web Terminal**: A browser-based terminal. +* **SSH**: A secure connection from your local machine. -## Connect to your training environment - -After your pod is deployed and active, you can connect using any of these methods: - -1. Go to your Fine Tuning pod dashboard - -2. Click **Connect** and choose your preferred connection method: - - * **Jupyter Notebook**: Browser-based notebook interface - * **Web Terminal**: Browser-based terminal - * **SSH**: Local machine terminal connection - - - -To use SSH, add your public SSH key in your account settings. The system automatically adds your key to the pod's `authorized_keys` file. - - + +To use SSH, add your public SSH key in your account settings. The system automatically adds your key to the Pod's `authorized_keys` file. For more information, see [Connect to a Pod with SSH](/pods/configuration/use-ssh). + + + ## Configure your environment -Your training environment includes this directory structure in `/workspace/fine-tuning/`: - -* `examples/`: Sample configurations and scripts -* `outputs/`: Training results and model outputs -* `config.yaml`: Training parameters for your model - -The system generates an initial `config.yaml` based on your selected base model and dataset. +Your training environment is located in the `/workspace/fine-tuning/` directory and has the following structure: -## Review and modify the configuration +* `examples/`: Sample configurations and scripts. +* `outputs/`: Where your training results and model outputs will be saved. +* `config.yaml`: The main configuration file for your training parameters. -The `config.yaml` file controls your fine-tuning parameters. Here's how to customize it: +The system generates an initial `config.yaml` based on your selected base model and dataset. This is where you define all the hyperparameters for your fine-tuning job. You may need to experiment with these settings to achieve the best results: -1. Open the configuration file: +Open the configuration file (`config.yaml`) in JupyterLab or using your preferred text editor to review and adjust the fine-tuning parameters: - ```sh - nano config.yaml - ``` - -2. Review and adjust the parameters based on your specific use case +```sh +nano config.yaml +``` -Here's an example configuration with common parameters: +Below is an example with common settings. You can replace the `NousResearch/Meta-Llama-3-8B` and `tatsu-lab/alpaca` with whichever base model and dataset you selected: ```yaml base_model: NousResearch/Meta-Llama-3.1-8B @@ -101,7 +100,7 @@ strict: false # Dataset configuration datasets: - - path: tatsu-lab/alpaca + - path: tatsu-lab/alpaca type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.05 @@ -136,7 +135,7 @@ tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: - use_reentrant: false + use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 @@ -153,51 +152,51 @@ weight_decay: 0.0 fsdp: fsdp_config: special_tokens: - pad_token: <|end_of_text|> + pad_token: <|end_of_text|> ``` - - -The `config.yaml` file contains all hyperparameters needed for fine-tuning. You may need to iterate on these settings to achieve optimal results. - - - -For more configuration examples, visit the [Axolotl examples repository](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples). +For more configuration examples, refer to the [Axolotl examples repository](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples). ## Start the fine-tuning process -Once your configuration is ready, follow these steps: +Once you're satisfied with your configuration, you can start the training. -1. Start the training process: +Run the following command in your terminal: - ```sh - axolotl train config.yaml - ``` +```sh +axolotl train config.yaml +``` -2. Monitor the training progress in your terminal +Monitor the training progress in your terminal. The output will show the training loss, validation loss, and other metrics. ## Push your model to Hugging Face -After completing the fine-tuning process, you can share your model: +After the fine-tuning process is complete, you can upload your model to the Hugging Face Hub to share it with the community or use it in your applications. -1. Log in to Hugging Face: + + - ```sh - huggingface-cli login - ``` +Run this command to log in to your Hugging Face account: -2. Create a new repository on Hugging Face if needed +```sh +huggingface-cli login +``` + + +To upload your model files to the Hugging Face Hub, run this command: -3. Upload your model: +```sh +huggingface-cli upload YOUR_USERNAME/MODEL_NAME ./outputs/out +``` +Replace `YOUR_USERNAME` with your Hugging Face username and `MODEL_NAME` with your desired model name. - ```sh - huggingface-cli upload / ./output - ``` + + -Replace `` with your Hugging Face username and `` with your desired model name. +## Next steps -## Additional resources +Now that you have a fine-tuned model, you can deploy it on Runpod for inference: -For more information about fine-tuning with Axolotl, see: +* **[Serverless](/serverless/overview)**: Deploy your model as a Serverless endpoint for a pay-as-you-go, scalable solution. +* **[Pods](/pods/overview)**: Run your model on a dedicated Pod for more control and persistent workloads. -* [Axolotl Documentation](https://github.com/OpenAccess-AI-Collective/axolotl)