llamaindex support for xinference cpu

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LlamaIndex does support Xorbits Inference (Xinference) for deploying local LLMs. However, the information extracts do not provide specific details about CPU support or requirements.

To use Xinference with LlamaIndex, you need to install Xinference using pip and then launch the local models. Here is a brief guide on how to do it:

1. Install Xinference:

1. After installation, restart your Jupyter notebook.
2. Run xinference in a new terminal window. You should see an output similar to this:

1. Locate the endpoint port number in the endpoint description. In the example above, it is 9997.
2. Set the port number with the following cell:

1. Launch a model and use it. Here is an example of how to do it:

```python

Define a client to send commands to xinference

client = RESTfulClient(f")

Download and Launch a model, this may take a while the first time

model_uid = client.launch_model(
model_name="llama-2-chat",
model_size_in_billions=7,
model_format="ggmlv3",
quantization="q2_K",
)

Initiate Xinference object to use the LLM

llm = Xinference(
endpoint=f"
model_uid=model_uid,
temperature=0.0,
max_tokens=512,
)
```
For more details, you can refer to the Xinference Local Deployment Guide () and the LlamaIndex GitHub page ().
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@kapa.ai how can I install in cluster and make use for inference

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Based on the information extracts provided, there are no specific instructions or details on how to install LlamaIndex or Xorbits Inference (Xinference) in a cluster for inference. The extracts only provide information on how to install and use Xinference locally.

For cluster deployment and usage, it would be best to refer to the official documentation or support channels of LlamaIndex and Xinference. They might provide more detailed and specific instructions on how to set up and use their software in a cluster environment.

Here are the links to the relevant pages for your reference:

• Xinference Local Deployment Guide ()
• LlamaIndex GitHub page ()

Please note that these links lead to local deployment guides. For cluster deployment, you might need additional steps or configurations that are not covered in these guides.
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@kapa.ai by using xorbitsai xinference in cpu cluster will my inference time reduces ?

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The information extracts provided do not contain specific details about the performance or inference time of Xorbits Inference (Xinference) when used in a CPU cluster.

Generally, deploying a model across a cluster of CPUs can potentially reduce inference time, as the workload can be distributed across multiple processing units. However, the actual performance can depend on various factors, including the specific configuration of the cluster, the model being used, and the nature of the tasks being performed.

For more accurate information, it would be best to refer to the official documentation or support channels of XorbitsAI Xinference. They might provide more detailed information on performance expectations and how to optimize the use of Xinference in a CPU cluster.
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