mirror of
https://github.com/open-webui/docs.git
synced 2026-01-02 09:49:38 +07:00
refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -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**
|
||||
|
||||
|
||||
|
||||
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**
|
||||
|
||||
|
||||
|
||||
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**
|
||||
|
||||
|
||||
:::
|
||||
|
||||
---
|
||||
|
||||
## 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! 🚀
|
||||
|
||||
Reference in New Issue
Block a user