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# OpenAI Compatible Server
`llama-cpp-python` offers an OpenAI API compatible web server.
This web server can be used to serve local models and easily connect them to existing clients.
## Setup
### Installation
The server can be installed by running the following command:
``` bash
pip install llama-cpp-python[ server]
```
### Running the server
The server can then be started by running the following command:
``` bash
python3 -m llama_cpp.server --model <model_path>
```
### Server options
For a full list of options, run:
``` bash
python3 -m llama_cpp.server --help
```
NOTE: All server options are also available as environment variables. For example, `--model` can be set by setting the `MODEL` environment variable.
## Guides
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### Code Completion
`llama-cpp-python` supports code completion via GitHub Copilot.
* NOTE * : Without GPU acceleration this is unlikely to be fast enough to be usable.
You'll first need to download one of the available code completion models in GGUF format:
- [replit-code-v1_5-GGUF ](https://huggingface.co/abetlen/replit-code-v1_5-3b-GGUF )
Then you'll need to run the OpenAI compatible web server with a increased context size substantially for GitHub Copilot requests:
``` bash
python3 -m llama_cpp.server --model <model_path> --n_ctx 16192
```
Then just update your settings in `.vscode/settings.json` to point to your code completion server:
``` json
{
// ...
"github.copilot.advanced" : {
"debug.testOverrideProxyUrl" : "http://<host>:<port>" ,
"debug.overrideProxyUrl" : "http://<host>:<port>"
}
// ...
}
```
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### Function Calling
`llama-cpp-python` supports structured function calling based on a JSON schema.
You'll first need to download one of the available function calling models in GGUF format:
- [functionary-7b-v1 ](https://huggingface.co/abetlen/functionary-7b-v1-GGUF )
Then when you run the server you'll need to also specify the `functionary-7b-v1` chat_format
``` bash
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python3 -m llama_cpp.server --model <model_path> --chat_format functionary
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```
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Check out the example notebook [here ](https://github.com/abetlen/llama-cpp-python/blob/main/examples/notebooks/Functions.ipynb ) for a walkthrough of some interesting use cases for function calling.
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### Multimodal Models
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`llama-cpp-python` supports the llava1.5 family of multi-modal models which allow the language model to
read information from both text and images.
You'll first need to download one of the available multi-modal models in GGUF format:
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- [llava-v1.5-7b ](https://huggingface.co/mys/ggml_llava-v1.5-7b )
- [llava-v1.5-13b ](https://huggingface.co/mys/ggml_llava-v1.5-13b )
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- [bakllava-1-7b ](https://huggingface.co/mys/ggml_bakllava-1 )
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Then when you run the server you'll need to also specify the path to the clip model used for image embedding and the `llava-1-5` chat_format
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``` bash
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python3 -m llama_cpp.server --model <model_path> --clip_model_path <clip_model_path> --chat_format llava-1-5
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```
Then you can just use the OpenAI API as normal
``` python3
from openai import OpenAI
client = OpenAI ( base_url = " http://<host>:<port>/v1 " , api_key = " sk-xxx " )
response = client . chat . completions . create (
model = " gpt-4-vision-preview " ,
messages = [
{
" role " : " user " ,
" content " : [
{
" type " : " image_url " ,
" image_url " : {
" url " : " <image_url> "
} ,
} ,
{ " type " : " text " , " text " : " What does the image say " } ,
] ,
}
] ,
)
print ( response )
```
