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