7.5 KiB
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:
pip install llama-cpp-python[server]
Running the server
The server can then be started by running the following command:
python3 -m llama_cpp.server --model <model_path>
Server options
For a full list of options, run:
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.
Check out the server config reference below settings for more information on the available options.
CLI arguments and environment variables are available for all of the fields defined in ServerSettings and ModelSettings
Additionally the server supports configuration check out the configuration section for more information and examples.
Guides
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:
Then you'll need to run the OpenAI compatible web server with a increased context size substantially for GitHub Copilot requests:
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:
{
// ...
"github.copilot.advanced": {
"debug.testOverrideProxyUrl": "http://<host>:<port>",
"debug.overrideProxyUrl": "http://<host>:<port>"
}
// ...
}
Function Calling
llama-cpp-python supports structured function calling based on a JSON schema.
Function calling is completely compatible with the OpenAI function calling API and can be used by connecting with the official OpenAI Python client.
You'll first need to download one of the available function calling models in GGUF format:
Then when you run the server you'll need to also specify either functionary-v1 or functionary-v2 chat_format.
Note that since functionary requires a HF Tokenizer due to discrepancies between llama.cpp and HuggingFace's tokenizers as mentioned here, you will need to pass in the path to the tokenizer too. The tokenizer files are already included in the respective HF repositories hosting the gguf files.
python3 -m llama_cpp.server --model <model_path_to_functionary_v2_model> --chat_format functionary-v2 --hf_pretrained_model_name_or_path <model_path_to_functionary_v2_tokenizer>
Check out this example notebook for a walkthrough of some interesting use cases for function calling.
Multimodal Models
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:
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
python3 -m llama_cpp.server --model <model_path> --clip_model_path <clip_model_path> --chat_format llava-1-5
Then you can just use the OpenAI API as normal
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)
Configuration and Multi-Model Support
The server supports configuration via a JSON config file that can be passed using the --config_file parameter or the CONFIG_FILE environment variable.
python3 -m llama_cpp.server --config_file <config_file>
Config files support all of the server and model options supported by the cli and environment variables however instead of only a single model the config file can specify multiple models.
The server supports routing requests to multiple models based on the model parameter in the request which matches against the model_alias in the config file.
At the moment only a single model is loaded into memory at, the server will automatically load and unload models as needed.
{
"host": "0.0.0.0",
"port": 8080,
"models": [
{
"model": "models/OpenHermes-2.5-Mistral-7B-GGUF/openhermes-2.5-mistral-7b.Q4_K_M.gguf",
"model_alias": "gpt-3.5-turbo",
"chat_format": "chatml",
"n_gpu_layers": -1,
"offload_kqv": true,
"n_threads": 12,
"n_batch": 512,
"n_ctx": 2048
},
{
"model": "models/OpenHermes-2.5-Mistral-7B-GGUF/openhermes-2.5-mistral-7b.Q4_K_M.gguf",
"model_alias": "gpt-4",
"chat_format": "chatml",
"n_gpu_layers": -1,
"offload_kqv": true,
"n_threads": 12,
"n_batch": 512,
"n_ctx": 2048
},
{
"model": "models/ggml_llava-v1.5-7b/ggml-model-q4_k.gguf",
"model_alias": "gpt-4-vision-preview",
"chat_format": "llava-1-5",
"clip_model_path": "models/ggml_llava-v1.5-7b/mmproj-model-f16.gguf",
"n_gpu_layers": -1,
"offload_kqv": true,
"n_threads": 12,
"n_batch": 512,
"n_ctx": 2048
},
{
"model": "models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf",
"model_alias": "text-davinci-003",
"n_gpu_layers": -1,
"offload_kqv": true,
"n_threads": 12,
"n_batch": 512,
"n_ctx": 2048
},
{
"model": "models/replit-code-v1_5-3b-GGUF/replit-code-v1_5-3b.Q4_0.gguf",
"model_alias": "copilot-codex",
"n_gpu_layers": -1,
"offload_kqv": true,
"n_threads": 12,
"n_batch": 1024,
"n_ctx": 9216
}
]
}
The config file format is defined by the ConfigFileSettings class.
Server Options Reference
::: llama_cpp.server.settings.ConfigFileSettings options: show_if_no_docstring: true
::: llama_cpp.server.settings.ServerSettings options: show_if_no_docstring: true
::: llama_cpp.server.settings.ModelSettings options: show_if_no_docstring: true