> ## Documentation Index > Fetch the complete documentation index at: https://docs.tracebloc.io/llms.txt > Use this file to discover all available pages before exploring further. # Run Your Model > Train or score a model on a use case from the dashboard's Run tab: pick a model, pass its checks, configure and start. The **Run** tab trains a model on a use case, or scores one, without leaving the dashboard. You pick a model, the platform checks it, you configure the experiment and click **Start**. It is the quickest way to a first experiment. For full control in code, use the [notebook](https://docs.tracebloc.io/join-use-case/start-training.md) instead: both start the same kind of experiment. ## Open the Run tab Join the use case first: see [Join](https://docs.tracebloc.io/join-use-case/join-use-case.md). Then open the use case and do one of these: * Click **Run** in the use case's tab bar. * Click **Start training** in the Overview's **On this page** sidebar. * Click **Train a model** next to **Model Leaderboard** on the **Leaderboard** tab. * On an empty **Training** tab, click **How to train a model**. Each opens the **Run your model** panel over the page. Closing it with **Esc**, the close icon or a click outside keeps what you chose, so you can look at the use case and come back. The panel keeps one state per use case. The top of the panel shows the use case, its dataset (for example `134 images · 3 labels`) and its secure environments, with how many are online. Hover over the secure environments to see what each experiment gets there: GPU, CPU cores and memory. The panel runs a notebook that tracebloc starts for you on first use. If it reports that no notebook can be started, close a notebook you have open on another use case, then try again. ## Step 1: Select your model 1. Open **Model** and pick a template from the model zoo, or choose **Upload a .py from your computer…** (a `.py` or `.zip` file). You can also drop the file onto the field. The list shows only the templates that fit the use case's task; **View code** opens the chosen template on GitHub. 2. Optional: open **Pretrained weights** and pick the zoo's weights, or upload a `.safetensors`, `.pth` or `.pkl` file. Without weights, training starts from scratch. A template from the zoo runs with the zoo's weights or none: a weights file chosen next to a template is not loaded. The checks start as soon as you choose a model. There is no button to press: | Check | What it does | | - | - | | **Structure** | Reads the file's declarations: framework, task, entry point and the constants this task needs. Nothing runs. | | **Security** | Scans the file before anything runs: only [allowed imports](https://docs.tracebloc.io/join-use-case/model-imports.md), no file, process, network or unpickling calls, no downloads. | | **Trainable** | Builds the model, runs one training step on synthetic data shaped like this task, then saves and reloads its weights. | | **Dataset compatible** | Compares the model with this dataset: classes, features, input channels, image size, sequence length, tokenizer, and memory on the smallest secure environment. | | **Weights** | Checks that uploaded weights belong to this model: no unexpected or missing keys, matching shapes. | **Model checks passed** means you can go on. When a check fails, a table lists each finding with the parameter, what was expected, what was found and the fix. Some fixes have a button that applies them for you, for example writing the right `output_classes` into the model file. Fix the file and choose it again: changing a file runs the checks again. **Raw output** shows the checker's own text. If **Trainable** keeps failing for no reason in your model, run it again; if it still fails, the hint names the cause, for example a notebook package older than the check. ## Step 2: Configure your run Step 2 opens once the model passes its checks. Choose **Train** or **Inference** at its top right: * **Train** trains the model across the secure environments. With pretrained weights, it fine-tunes them. The settings are a short Python cell: `training` is your model, linked to the dataset, and typing `training.` lists every setting. They are the same settings as in the notebook: see [Hyperparameters](https://docs.tracebloc.io/join-use-case/hyperparameters.md). Click **Validate configuration**. The result line shows the plan that applies, for example the cycles, epochs and batch size, and its estimated cost in PetaFLOPs (PF). After an edit it reads **Edited, not applied · validate again**. * **Inference** scores the model on the use case's test data without training it. There is nothing to configure: the line shows the estimated cost. Without weights, it scores the zoo's own weights, if any, or random weights as a baseline. Inference counts against your team's daily submissions, which reset at 00:00 UTC. ## Step 3: Start Click **Start**. When **Start** is greyed out, hover over it to see why, for example **Validate the configuration** or **No edge holding this data is online**. If some secure environments are offline, you can still start: a warning names the data that is left out. Once the experiment starts, the panel shows its key: * After **Train**, click **Monitor in training** to follow it on the **Training** tab. See [Start Training](https://docs.tracebloc.io/join-use-case/start-training.md#5-start-training) for pausing, resuming and stopping it. * After **Inference**, click **See the scores**. The result is a submission labelled **Inference only** on the **Leaderboard**. If the platform refuses the start, the panel shows why. **Not enough petaflops left for this run** means your team's compute budget is used up: see [When the compute budget runs out](https://docs.tracebloc.io/join-use-case/start-training.md#when-the-compute-budget-runs-out). ## Run again After a start, **Run again** replaces **Start**. It takes you back to step 2 with the same model, its checks, its weights and your settings, so you can change a setting and start another experiment. After **Train**, click **Validate configuration** again before **Start**. The first experiment keeps running. **Start over** (on the panel's title line) clears the model, the weights and the settings. It does not stop an experiment you started: stop that from the **Training** tab. ## When to use the notebook instead The Run tab covers one model file, its weights and the training settings. Use the [notebook](https://docs.tracebloc.io/join-use-case/start-training.md), locally or in Google Colab, when you want to script several experiments, keep your code in your own files, or use SDK calls the panel doesn't offer. *** ## Need Help? * Email us at [support@tracebloc.io](mailto:support@tracebloc.io) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.