63 lines
3.2 KiB
Markdown
63 lines
3.2 KiB
Markdown
# Connecting to Xinference Local Deployed Models
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> 🚧 WIP
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[Xorbits inference](https://github.com/xorbitsai/inference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models, and can even be used on laptops. It supports various models compatible with GGML, such as chatglm, baichuan, whisper, vicuna, orca, etc.
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And Dify supports connecting to Xinference deployed large language model inference and embedding capabilities locally.
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## Deploy Xinference
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There are two ways to deploy Xinference, namely [local deployment](https://github.com/xorbitsai/inference/blob/main/README.md#local) and [distributed deployment](https://github.com/xorbitsai/inference/blob/main/README.md#distributed), here we take local deployment as an example.
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1. First, install Xinference via PyPI:
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```bash
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$ pip install "xinference[all]"
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```
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2. Start Xinference locally:
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```bash
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$ xinference
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2023-08-20 19:21:05,265 xinference 10148 INFO Xinference successfully started. Endpoint: http://127.0.0.1:9997
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2023-08-20 19:21:05,266 xinference.core.supervisor 10148 INFO Worker 127.0.0.1:37822 has been added successfully
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2023-08-20 19:21:05,267 xinference.deploy.worker 10148 INFO Xinference worker successfully started.
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```
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Xinference will start a worker locally by default, with the endpoint: `http://127.0.0.1:9997`, and the default port is `9997`.
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To modify the host or port, you can refer to xinference's help information: `xinference --help`.
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3. Create and deploy the model
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Visit `http://127.0.0.1:9997`, select the model and specification you need to deploy, and click `Create` to create and deploy the model, as shown below:
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<figure><img src="../../.gitbook/assets/xinference-webpage.png" alt=""><figcaption></figcaption></figure>
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As different models have different compatibility on different hardware platforms, please refer to [Xinference built-in models](https://inference.readthedocs.io/en/latest/models/builtin/index.html) to ensure the created model supports the current hardware platform.
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4. Obtain the model UID
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Return to the command line interface and enter:
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```bash
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$ xinference list
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UID Type Name Format Size (in billions) Quantization
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------------------------------------ ------ ----------- -------- -------------------- --------------
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a9e4d530-3f4b-11ee-a9b9-e6608f0bd69a LLM vicuna-v1.3 ggmlv3 7 q2_K
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```
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The first column is the model UID created in step 3, such as `a9e4d530-3f4b-11ee-a9b9-e6608f0bd69a` above.
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5. After the model is deployed, connect the deployed model in Dify.
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In `Settings > Model Providers > Xinference`, enter:
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- Model name: `vicuna-v1.3`
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- Server URL: `http://127.0.0.1:9997`
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- Model UID: `a9e4d530-3f4b-11ee-a9b9-e6608f0bd69a`
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Click "Save" to use the model in the dify application.
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Dify also supports using [Xinference builtin models](https://github.com/xorbitsai/inference/blob/main/README.md#builtin-models) as Embedding models, just select the Embeddings type in the configuration box.
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For more information about Xinference, please refer to: [Xorbits Inference](https://github.com/xorbitsai/inference) |