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docs/tutorials/images.md
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@@ -160,7 +160,7 @@ Using Azure OpenAI Dall-E directly is unsupported, but you can [set up a LiteLLM
## Using Image Generation
![Image Generation Tutorial](/img/tutorial_image_generation.png)
![Image Generation Tutorial](/images/tutorial_image_generation.png)
1. First, use a text generation model to write a prompt for image generation.
2. After the response has finished, you can click the Picture icon to generate an image.

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docs/tutorials/integrations/deepseekr1-dynamic.md
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@@ -0,0 +1,169 @@
---
sidebar_position: 1
title: "🦥 Run DeepSeek R1 Dynamic 1.58-bit with Llama.cpp"
---
A huge shoutout to **UnslothAI** for their incredible efforts! Thanks to their hard work, we can now run the **full DeepSeek-R1** 671B parameter model in its dynamic 1.58-bit quantized form (compressed to just 131GB) on **Llama.cpp**! And the best part? You no longer have to despair about needing massive enterprise-class GPUs or servers — it’s possible to run this model on your personal machine (albeit slowly for most consumer hardware).
:::note
The only true **DeepSeek-R1** model on Ollama is the **671B version** available here: [https://ollama.com/library/deepseek-r1:671b](https://ollama.com/library/deepseek-r1:671b). Other versions are **distilled** models.
:::
This guide focuses on running the **full DeepSeek-R1 Dynamic 1.58-bit quantized model** using **Llama.cpp** integrated with **Open WebUI**. For this tutorial, we’ll demonstrate the steps with an **M4 Max + 128GB RAM** machine. You can adapt the settings to your own configuration.
---
## Step 1: Install Llama.cpp
You can either:
- [Download the prebuilt binaries](https://github.com/ggerganov/llama.cpp/releases)
- **Or build it yourself**: Follow the instructions here: [Llama.cpp Build Guide](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
## Step 2: Download the Model Provided by UnslothAI
Head over to [Unsloth’s Hugging Face page](https://huggingface.co/unsloth/DeepSeek-R1-GGUF) and download the appropriate **dynamic quantized version** of DeepSeek-R1. For this tutorial, we’ll use the **1.58-bit (131GB)** version, which is highly optimized yet remains surprisingly functional.
:::tip
Know your "working directory" — where your Python script or terminal session is running. The model files will download to a subfolder of that directory by default, so be sure you know its path! For example, if you're running the command below in `/Users/yourname/Documents/projects`, your downloaded model will be saved under `/Users/yourname/Documents/projects/DeepSeek-R1-GGUF`.
:::
To understand more about UnslothAI’s development process and why these dynamic quantized versions are so efficient, check out their blog post: [UnslothAI DeepSeek R1 Dynamic Quantization](https://unsloth.ai/blog/deepseekr1-dynamic).
Here’s how to download the model programmatically:
```python
# Install Hugging Face dependencies before running this:
# pip install huggingface_hub hf_transfer
from huggingface_hub import snapshot_download
snapshot_download(
repo_id = "unsloth/DeepSeek-R1-GGUF", # Specify the Hugging Face repo
local_dir = "DeepSeek-R1-GGUF", # Model will download into this directory
allow_patterns = ["*UD-IQ1_S*"], # Only download the 1.58-bit version
)
```
Once the download completes, you’ll find the model files in a directory structure like this:
```
DeepSeek-R1-GGUF/
├── DeepSeek-R1-UD-IQ1_S/
│ ├── DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf
│ ├── DeepSeek-R1-UD-IQ1_S-00002-of-00003.gguf
│ ├── DeepSeek-R1-UD-IQ1_S-00003-of-00003.gguf
```
:::info
🛠️ Update paths in the later steps to **match your specific directory structure**. For example, if your script was in `/Users/tim/Downloads`, the full path to the GGUF file would be:
`/Users/tim/Downloads/DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf`.
:::
## Step 3: Make Sure Open WebUI is Installed and Running
If you don’t already have **Open WebUI** installed, no worries! It’s a simple setup. Just follow the [Open WebUI documentation here](https://docs.openwebui.com/). Once installed, start the application — we’ll connect it in a later step to interact with the DeepSeek-R1 model.
## Step 4: Serve the Model Using Llama.cpp
Now that the model is downloaded, the next step is to run it using **Llama.cpp’s server mode**. Before you begin:
1. **Locate the `llama-server` binary.**
If you built from source (as outlined in Step 1), the `llama-server` executable will be located in `llama.cpp/build/bin`. Navigate to this directory by using the `cd` command:
```bash
cd [path-to-llama-cpp]/llama.cpp/build/bin
```
Replace `[path-to-llama-cpp]` with the location where you cloned or built Llama.cpp. For example:
```bash
cd ~/Documents/workspace/llama.cpp/build/bin
```
2. **Point to your model folder.**
Use the full path to the downloaded GGUF files created in Step 2. When serving the model, specify the first part of the split GGUF files (e.g., `DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf`).
Here’s the command to start the server:
```bash
./llama-server \
--model /[your-directory]/DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \
--port 10000 \
--ctx-size 1024 \
--n-gpu-layers 40
```
> 🔑 **Parameters to Customize Based on Your Machine:**
> - **`--model`:** Replace `/[your-directory]/` with the path where the GGUF files were downloaded in Step 2.
> - **`--port`:** The server default is `8080`, but feel free to change it based on your port availability.
> - **`--ctx-size`:** Determines context length (number of tokens). You can increase it if your hardware allows, but be cautious of rising RAM/VRAM usage.
> - **`--n-gpu-layers`:** Set the number of layers you want to offload to your GPU for faster inference. The exact number depends on your GPU’s memory capacity — reference Unsloth’s table for specific recommendations. For CPU-only setups, set it to `0`.
For example, if your model was downloaded to `/Users/tim/Documents/workspace` and you have an RTX 4090 GPU with 24GB VRAM, your command would look like this:
```bash
./llama-server \
--model /Users/tim/Documents/workspace/DeepSeek-R1-GGUF/DeepSeek-R1-UD-IQ1_S/DeepSeek-R1-UD-IQ1_S-00001-of-00003.gguf \
--port 10000 \
--ctx-size 1024 \
--n-gpu-layers 40
```
Once the server starts, it will host a **local OpenAI-compatible API** endpoint at:
```
http://127.0.0.1:10000
```
:::info
🖥️ **Llama.cpp Server Running**
![Server Screenshot](/images/tutorials/deepseek/serve.png)
After running the command, you should see a message confirming the server is active and listening on port 10000.
:::
Be sure to **keep this terminal session running**, as it serves the model for all subsequent steps.
## Step 5: Connect Llama.cpp to Open WebUI
1. Go to **Admin Settings** in Open WebUI.
2. Navigate to **Connections > OpenAI Connections.**
3. Add the following details for the new connection:
- URL: `http://127.0.0.1:10000/v1`
- API Key: `none`
:::info
🖥️ **Adding Connection in Open WebUI**
![Connection Screenshot](/images/tutorials/deepseek/connection.png)
After running the command, you should see a message confirming the server is active and listening on port 10000.
:::
Once the connection is saved, you can start querying **DeepSeek-R1** directly from Open WebUI! 🎉
---
## Example: Generating Responses
You can now use Open WebUI’s chat interface to interact with the **DeepSeek-R1 Dynamic 1.58-bit model**.
:::info
🖥️ **DeepSeek-R1 Response in Open WebUI**
![Response Screenshot](/images/tutorials/deepseek/response.png)
:::
---
## Notes and Considerations
- **Performance:**
Running a massive 131GB model like DeepSeek-R1 on personal hardware will be **slow**. Even with our M4 Max (128GB RAM), inference speeds were modest. But the fact that it works at all is a testament to UnslothAI’s optimizations.
- **VRAM/Memory Requirements:**
Ensure sufficient VRAM and system RAM for optimal performance. With low-end GPUs or CPU-only setups, expect slower speeds (but it’s still doable!).
---
Thanks to **UnslothAI** and **Llama.cpp**, running one of the largest open-source reasoning models, **DeepSeek-R1** (1.58-bit version), is finally accessible to individuals. While it’s challenging to run such models on consumer hardware, the ability to do so without massive computational infrastructure is a significant technological milestone.
⭐ Big thanks to the community for pushing the boundaries of open AI research.
Happy experimenting! 🚀

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docs/tutorials/tips/contributing-tutorial.md
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@@ -60,7 +60,7 @@ b. **Modify `docusaurus.config.ts` to Use Environment Variables**
const config: Config = {
title: "Open WebUI",
tagline: "ChatGPT-Style WebUI for LLMs (Formerly Ollama WebUI)",
favicon: "img/favicon.png",
favicon: "images/favicon.png",
url: process.env.SITE_URL || "https://openwebui.com",
baseUrl: process.env.BASE_URL || "/",
...

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docs/tutorials/web_search.md
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@@ -348,7 +348,7 @@ docker exec -it open-webui curl http://host.docker.internal:8080/search?q=this+i
5. Adjust the `Search Result Count` and `Concurrent Requests` values accordingly
6. Save changes
![SearXNG GUI Configuration](/img/tutorial_searxng_config.png)
![SearXNG GUI Configuration](/images/tutorial_searxng_config.png)
## 5. Using Web Search in a Chat
@@ -356,7 +356,7 @@ To access Web Search, Click on the + next to the message input field.
Here you can toggle Web Search On/Off.
![Web Search UI Toggle](/img/web_search_toggle.png)
![Web Search UI Toggle](/images/web_search_toggle.png)
By following these steps, you will have successfully set up SearXNG with Open WebUI, enabling you to perform web searches using the SearXNG engine.
@@ -379,14 +379,14 @@ This is enabled on a per session basis eg. reloading the page, changing to anoth
7. Fill `Google PSE API Key` with the `API key` and `Google PSE Engine Id` (# 4)
8. Click `Save`
![Open WebUI Admin panel](/img/tutorial_google_pse1.png)
![Open WebUI Admin panel](/images/tutorial_google_pse1.png)
#### Note
You have to enable `Web search` in the prompt field, using plus (`+`) button.
Search the web ;-)
![enable Web search](/img/tutorial_google_pse2.png)
![enable Web search](/images/tutorial_google_pse2.png)
## Brave API
@@ -444,13 +444,13 @@ services:
6. [Optional] Enter the `SearchApi engine` name you want to query. Example, `google`, `bing`, `baidu`, `google_news`, `bing_news`, `google_videos`, `google_scholar` and `google_patents.` By default, it is set to `google`.
7. Click `Save`.
![Open WebUI Admin panel](/img/tutorial_searchapi_search.png)
![Open WebUI Admin panel](/images/tutorial_searchapi_search.png)
#### Note
You have to enable `Web search` in the prompt field, using plus (`+`) button to search the web using [SearchApi](https://www.searchapi.io/) engines.
![enable Web search](/img/enable_web_search.png)
![enable Web search](/images/enable_web_search.png)
## Kagi API