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sidebar_position: 5
slug: /deploy_local_llm
---
# Deploy a local LLM
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RAGFlow supports deploying models locally using Ollama or Xinference. If you have locally deployed models to leverage or wish to enable GPU or CUDA for inference acceleration, you can bind Ollama or Xinference into RAGFlow and use either of them as a local "server" for interacting with your local models.
RAGFlow seamlessly integrates with Ollama and Xinference, without the need for further environment configurations. You can use them to deploy two types of local models in RAGFlow: chat models and embedding models.
:::tip NOTE
This user guide does not intend to cover much of the installation or configuration details of Ollama or Xinference; its focus is on configurations inside RAGFlow. For the most current information, you may need to check out the official site of Ollama or Xinference.
:::
# Deploy a local model using jina
[Jina](https://github.com/jina-ai/jina) lets you build AI services and pipelines that communicate via gRPC, HTTP and WebSockets, then scale them up and deploy to production.
To deploy a local model, e.g., **gpt2**, using Jina:
### 1. Check firewall settings
Ensure that your host machine's firewall allows inbound connections on port 12345.
```bash
sudo ufw allow 12345/tcp
```
### 2.install jina package
```bash
pip install jina
```
### 3. deployment local model
Step 1: Navigate to the rag/svr directory.
```bash
cd rag/svr
```
Step 2: Use Python to run the jina_server.py script and pass in the model name or the local path of the model (the script only supports loading models downloaded from Huggingface)
```bash
python jina_server.py --model_name gpt2
```
## Deploy a local model using Ollama
[Ollama](https://github.com/ollama/ollama) enables you to run open-source large language models that you deployed locally. It bundles model weights, configurations, and data into a single package, defined by a Modelfile, and optimizes setup and configurations, including GPU usage.
:::note
- For information about downloading Ollama, see [here](https://github.com/ollama/ollama?tab=readme-ov-file#ollama).
- For information about configuring Ollama server, see [here](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server).
- For a complete list of supported models and variants, see the [Ollama model library](https://ollama.com/library).
:::
To deploy a local model, e.g., **Llama3**, using Ollama:
### 1. Check firewall settings
Ensure that your host machine's firewall allows inbound connections on port 11434. For example:
```bash
sudo ufw allow 11434/tcp
```
### 2. Ensure Ollama is accessible
Restart system and use curl or your web browser to check if the service URL of your Ollama service at `http://localhost:11434` is accessible.
```bash
Ollama is running
```
### 3. Run your local model
```bash
ollama run llama3
```
If your Ollama is installed through Docker, run the following instead:
```bash
docker exec -it ollama ollama run llama3
```
### 4. Add Ollama
In RAGFlow, click on your logo on the top right of the page **>** **Model Providers** and add Ollama to RAGFlow:
### 5. Complete basic Ollama settings
In the popup window, complete basic settings for Ollama:
1. Because **llama3** is a chat model, choose **chat** as the model type.
2. Ensure that the model name you enter here *precisely* matches the name of the local model you are running with Ollama.
3. Ensure that the base URL you enter is accessible to RAGFlow.
4. OPTIONAL: Switch on the toggle under **Does it support Vision?** if your model includes an image-to-text model.
:::caution NOTE
- If your Ollama and RAGFlow run on the same machine, use `http://localhost:11434` as base URL.
- If your Ollama and RAGFlow run on the same machine and Ollama is in Docker, use `http://host.docker.internal:11434` as base URL.
- If your Ollama runs on a different machine from RAGFlow, use `http://:11434` as base URL.
:::
:::danger WARNING
If your Ollama runs on a different machine, you may also need to set the `OLLAMA_HOST` environment variable to `0.0.0.0` in **ollama.service** (Note that this is *NOT* the base URL):
```bash
Environment="OLLAMA_HOST=0.0.0.0"
```
See [this guide](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-do-i-configure-ollama-server) for more information.
:::
:::caution WARNING
Improper base URL settings will trigger the following error:
```bash
Max retries exceeded with url: /api/chat (Caused by NewConnectionError(': Failed to establish a new connection: [Errno 111] Connection refused'))
```
:::
### 6. Update System Model Settings
Click on your logo **>** **Model Providers** **>** **System Model Settings** to update your model:
*You should now be able to find **llama3** from the dropdown list under **Chat model**.*
> If your local model is an embedding model, you should find your local model under **Embedding model**.
### 7. Update Chat Configuration
Update your chat model accordingly in **Chat Configuration**:
> If your local model is an embedding model, update it on the configruation page of your knowledge base.
## Deploy a local model using Xinference
Xorbits Inference ([Xinference](https://github.com/xorbitsai/inference)) enables you to unleash the full potential of cutting-edge AI models.
:::note
- For information about installing Xinference Ollama, see [here](https://inference.readthedocs.io/en/latest/getting_started/).
- For a complete list of supported models, see the [Builtin Models](https://inference.readthedocs.io/en/latest/models/builtin/).
:::
To deploy a local model, e.g., **Mistral**, using Xinference:
### 1. Check firewall settings
Ensure that your host machine's firewall allows inbound connections on port 9997.
### 2. Start an Xinference instance
```bash
$ xinference-local --host 0.0.0.0 --port 9997
```
### 3. Launch your local model
Launch your local model (**Mistral**), ensuring that you replace `${quantization}` with your chosen quantization method:
```bash
$ xinference launch -u mistral --model-name mistral-v0.1 --size-in-billions 7 --model-format pytorch --quantization ${quantization}
```
### 4. Add Xinference
In RAGFlow, click on your logo on the top right of the page **>** **Model Providers** and add Xinference to RAGFlow:
### 5. Complete basic Xinference settings
Enter an accessible base URL, such as `http://:9997/v1`.
> For rerank model, please use the `http://:9997/v1/rerank` as the base URL.
### 6. Update System Model Settings
Click on your logo **>** **Model Providers** **>** **System Model Settings** to update your model.
*You should now be able to find **mistral** from the dropdown list under **Chat model**.*
> If your local model is an embedding model, you should find your local model under **Embedding model**.
### 7. Update Chat Configuration
Update your chat model accordingly in **Chat Configuration**:
> If your local model is an embedding model, update it on the configruation page of your knowledge base.
## Deploy a local model using IPEX-LLM
[IPEX-LLM](https://github.com/intel-analytics/ipex-llm) is a PyTorch library for running LLMs on local Intel CPUs or GPUs (including iGPU or discrete GPUs like Arc, Flex, and Max) with low latency. It supports Ollama on Linux and Windows systems.
To deploy a local model, e.g., **Qwen2**, using IPEX-LLM-accelerated Ollama:
### 1. Check firewall settings
Ensure that your host machine's firewall allows inbound connections on port 11434. For example:
```bash
sudo ufw allow 11434/tcp
```
### 2. Launch Ollama service using IPEX-LLM
#### 2.1 Install IPEX-LLM for Ollama
:::tip NOTE
IPEX-LLM's supports Ollama on Linux and Windows systems.
:::
For detailed information about installing IPEX-LLM for Ollama, see [Run llama.cpp with IPEX-LLM on Intel GPU Guide](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md):
- [Prerequisites](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md#0-prerequisites)
- [Install IPEX-LLM cpp with Ollama binaries](https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md#1-install-ipex-llm-for-llamacpp)
*After the installation, you should have created a Conda environment, e.g., `llm-cpp`, for running Ollama commands with IPEX-LLM.*
#### 2.2 Initialize Ollama
1. Activate the `llm-cpp` Conda environment and initialize Ollama:
```bash
conda activate llm-cpp
init-ollama
```
Run these commands with *administrator privileges in Miniforge Prompt*:
```cmd
conda activate llm-cpp
init-ollama.bat
```
2. If the installed `ipex-llm[cpp]` requires an upgrade to the Ollama binary files, remove the old binary files and reinitialize Ollama using `init-ollama` (Linux) or `init-ollama.bat` (Windows).
*A symbolic link to Ollama appears in your current directory, and you can use this executable file following standard Ollama commands.*
#### 2.3 Launch Ollama service
1. Set the environment variable `OLLAMA_NUM_GPU` to `999` to ensure that all layers of your model run on the Intel GPU; otherwise, some layers may default to CPU.
2. For optimal performance on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), set the following environment variable before launching the Ollama service:
```bash
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
```
3. Launch the Ollama service:
```bash
export OLLAMA_NUM_GPU=999
export no_proxy=localhost,127.0.0.1
export ZES_ENABLE_SYSMAN=1
source /opt/intel/oneapi/setvars.sh
export SYCL_CACHE_PERSISTENT=1
./ollama serve
```
Run the following command *in Miniforge Prompt*:
```cmd
set OLLAMA_NUM_GPU=999
set no_proxy=localhost,127.0.0.1
set ZES_ENABLE_SYSMAN=1
set SYCL_CACHE_PERSISTENT=1
ollama serve
```
:::tip NOTE
To enable the Ollama service to accept connections from all IP addresses, use `OLLAMA_HOST=0.0.0.0 ./ollama serve` rather than simply `./ollama serve`.
:::
*The console displays messages similar to the following:*
### 3. Pull and Run Ollama model
#### 3.1 Pull Ollama model
With the Ollama service running, open a new terminal and run `./ollama pull ` (Linux) or `ollama.exe pull ` (Windows) to pull the desired model. e.g., `qwen2:latest`:
#### 3.2 Run Ollama model
```bash
./ollama run qwen2:latest
```
```cmd
ollama run qwen2:latest
```
### 4. Configure RAGflow
To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the configurations in RAGFlow. The steps are identical to those outlined in the *Deploy a local model using Ollama* section:
1. [Add Ollama](#4-add-ollama)
2. [Complete basic Ollama settings](#5-complete-basic-ollama-settings)
3. [Update System Model Settings](#6-update-system-model-settings)
4. [Update Chat Configuration](#7-update-chat-configuration)