Models

The Models section of the Workspace within Open WebUI is a powerful tool that allows you to create and manage custom models tailored to specific purposes.

While backends like Ollama have their own Modelfile format, Open WebUI employs a robust internal Preset System. This allows you to "wrap" any model (including GPT-4, Claude, or local Llama 3) to bind specific System Prompts, Knowledge Collections, Tools, and Dynamic Variables to it.

This section serves as a central hub for all your models, providing a range of features to edit, clone, share, export, and hide your custom agents.

Creating and Editing Models

When you create a new model or edit an existing one, you are building a configuration wrapper around a "Base Model". To access the model configuration interface, you have two primary entry points from the main Models list:

1. New Model: Click the + New Model button in the top-right corner. This opens a blank configuration page to create a preset from scratch.
2. Edit Model: Click the ellipsis (...) on an existing model card and select Edit. This opens the configuration page pre-filled with that model's current settings.

Both actions lead to the same Model Builder interface, where you can configure the settings below.

Core Configuration

• Avatar Photo: Upload a custom image to represent your model in the chat interface.
• Model Name & ID: The display name and unique identifier for your custom preset (e.g., "Python Tutor" or "Meeting Summarizer").
• Base Model: The actual model beneath the hood that powers the agent. You can choose any model connected to Open WebUI. You can create a custom preset for gpt-4o just as easily as llama3.
• Description: A short summary of what the model does.
• Tags: Add tags to organize models in the selector dropdown.
• Visibility & Groups:
  • Private: Restricts access to specific users or groups.
  • Groups Selector: Use the dropdown to grant access to specific teams (e.g., "Admins", "Developers") without making the model public to everyone.

System Prompt & Dynamic Variables

The System Prompt defines the behavior and persona of the model. Unlike standard prompts, Open WebUI supports Dynamic Variable Injection using Jinja2-style placeholders. This allows the model to be aware of time, date, and user details.

Variable	Description	Output Example
{{ CURRENT_DATE }}	Injects today's date (YYYY-MM-DD).	2024-10-27
{{ CURRENT_TIME }}	Injects the current time (24hr).	14:30:05
{{ USER_NAME }}	The display name of the logged-in user.	Admin

Example System Prompt:

You are a helpful assistant for {{ USER_NAME }}.
The current date is {{ CURRENT_DATE }}.

Advanced Parameters

Clicking Show on Advanced Params allows you to fine-tune the inference generation.

• Stop Sequences: A powerful feature that tells the model to force-stop generating text when it hits specific characters. This is vital for roleplay or coding models to prevent them from hallucinating both sides of a conversation.
  • Format: Enter the string (e.g., <|end_of_text|>, User:) and press Enter.
• Temperature, Top P, etc.: Adjust the creativity and determinism of the model.

Prompt Suggestions

Prompt Suggestions are clickable "starter chips" that appear above the input bar when a user opens a fresh chat with this model. These are vital for onboarding users to specialized agents.

• Purpose: To guide the user on what the model is capable of or to provide one-click shortcuts for common tasks.
• How to add: Type a phrase (e.g., "Summarize this text") and click the + button. You can add multiple suggestions.
• Example: For a "Python Tutor" model, you might add:
  • "Explain this code step-by-step"
  • "Find bugs in the following script"
  • "Write a Unit Test for this function"

Capabilities, Binding & Defaults

You can transform a generic model into a specialized agent by toggling specific capabilities and binding resources.

• Knowledge: Instead of manually selecting documents for every chat, you can bind a specific knowledgebase Collection or File to this model. Whenever this model is selected, RAG (Retrieval Augmented Generation) is automatically active for those specific files.
• Tools: Force specific tools to be enabled by default (e.g., always enable the Calculator tool for a "Math Bot").
• Filters: Attach specific Pipelines/Filters (e.g., a Profanity Filter or PII Redaction script) to run exclusively on this model.
• Actions: Attach actionable scripts like Add to Memories or Button triggers.
• Capabilities: Granularly control what the model is allowed to do:
  • Vision: Toggle to enable image analysis capabilities (requires a vision-capable Base Model).
  • Web Search: Enable the model to access the configured search provider (e.g., Google, SearxNG) for real-time information.
  • File Upload: Allow users to upload files to this model.
  • Code Interpreter: Enable Python code execution.
  • Image Generation: Enable image generation integration.
  • Usage / Citations: Toggle usage tracking or source citations.
  • Status Updates: Show visible progress steps in the chat UI (e.g., "Searching web...", "Reading file...") during generation. Useful for slower, complex tasks.
• Default Features: Force specific toggles (like Web Search) to be "On" immediately when a user starts a chat with this model.

Model Management

From the main list view in the Models section, click the ellipsis (...) next to any model to perform actions:

• Edit: Open the configuration panel for this model.
• Hide: Hide the model from the model selector dropdown within chats (useful for deprecated models) without deleting it.
• Clone: Create a copy of a model configuration, which will be appended with -clone.

note

You cannot clone a raw Base Model directly; you must create a custom model first before cloning it.

• Copy Link: Copies a direct URL to the model settings.
• Export: Download the model configuration as a .json file.
• Share: Share your model configuration with the Open WebUI community by clicking the Share button (redirects to openwebui.com).
• Delete: Permanently remove the preset.

Import and Export

• Import Models: Import models from a .json file or from Open WebUI community links.
• Export Models: Export all your custom model configurations into a single .json file for backup or migration.
• Discover a Model: At the bottom of the page, you can explore and download presets made by the Open WebUI community.

Downloading Raw Models

To download new raw Base Models (like Llama-3.2-3B-Instruct-GGUF:Q8_0 or Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M), navigate to Settings > Connections > Ollama. Alternatively, type ollama run hf.co/{username}/{repository}:{quantization} in the model selector to pull directly from Hugging Face. This action will create a button within the model selector labeled "Pull [Model Name]" that will begin downloading the model from its source once clicked.

Model Switching in Chat

Open WebUI allows for dynamic model switching and parallel inference within a chat session.

Example: Switching between Mistral, LLaVA, and GPT-4 in a Multi-Stage Task.

• Scenario: A multi-stage conversation involves different task types, such as starting with a simple FAQ, interpreting an image, and then generating a creative response.
• Reason for Switching: The user can leverage each model's specific strengths for each stage:
  • Mistral for general questions to reduce computation time and costs.
  • LLaVA for visual tasks to gain insights from image-based data.
  • GPT-4 for generating more sophisticated and nuanced language output.
• Process: The user switches between models, depending on the task type, to maximize efficiency and response quality.

How To:

1. Select the Model: Within the chat interface, select the desired models from the model switcher dropdown. You can select up to two models simultaneously, and both responses will be generated. You can then navigate between them by using the back and forth arrows.
2. Context Preservation: Open WebUI retains the conversation context across model switches, allowing smooth transitions.