Functionary is a language model that can interpret and execute functions/plugins.
The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
Documentation and more examples: functionary.meetkai.com
Changelog: (click to expand)
- [2024/06/14] We release meetkai/functionary-medium-v3.0 (based on meta-llama/Meta-Llama-3-70B-Instruct) with better capability for function calling
- [2024/05/17] We release meetkai/functionary-small-v2.5 with better capability for function calling and code interpreter compared with functionary-small-v2.4
- [2024/05/06] Streaming support for functionary v2 to v2.4 models is released in llama-cpp-python!
- [2024/05/03] Added support for serverless vLLM deployment on Modal.com
- [2024/04/27] New and improved grammar sampling! Ensures 100% accuracy in generating function names, prompt template and parameters.
- [2024/04/02] We release meetkai/functionary-small-v2.4 and meetkai/functionary-medium-v2.4! The first functionary models with code-interpreter ability (by passing in
{type: "code_interpreter"}in tools)!
To install the required dependencies, run:
pip install -r requirements.txtNow you can start a blazing fast vLLM server. requirements
Small Model:
python3 server_vllm.py --model "meetkai/functionary-small-v2.5" --host 0.0.0.0 --max-model-len 8192Medium model: (click to expand)
If you use multiple GPUs (medium models require: 4xA6000 or 2xA100 80GB to run), need to use: tensor-parallel-size
python3 server_vllm.py --model "meetkai/functionary-medium-v3.0" --max-model-len 8192 --tensor-parallel-size 2Grammar Sampling
We also offer our own function-calling grammar sampling feature which constrains the LLM's generation to always follow the prompt template, and ensures 100% accuracy for function name. The parameters are generated using the efficient lm-format-enforcer, which ensures that the parameters follow the schema of the tool called. To enable grammar sampling, run the vLLM server with the command-line argument --enable-grammar-sampling:
python3 server_vllm.py --model "meetkai/functionary-medium-v2.4" --max-model-len 8192 --tensor-parallel-size 2 --enable-grammar-samplingNote:
- Grammar Sampling support is applicable only for the V2 models. There is no such support for V1 models.
- Our vLLM server supports the
tool_choice="required"feature in OpenAI Chat Completion API exclusively only when grammar sampling is enabled.
Text-Generation-Inference
We also provide a service that performs inference on Functionary models using Text-Generation-Inference (TGI). Follow these steps to get started:
-
Install Docker following their installation instructions.
-
Install the Docker SDK for Python
pip install docker- Start up the Functionary TGI server
At start-up, the Functionary TGI server tries to connect to an existing TGI endpoint. In this case, you can run the following:
python3 server_tgi.py --model <REMOTE_MODEL_ID_OR_LOCAL_MODEL_PATH> --endpoint <TGI_SERVICE_ENDPOINT>If the TGI endpoint does not exist, the Functionary TGI server will start a new TGI endpoint container with the address provided in the endpoint CLI argument via the installed Docker Python SDK. Run the following commands for remote and local models respectively:
python3 server_tgi.py --model <REMOTE_MODEL_ID> --remote_model_save_folder <PATH_TO_SAVE_AND_CACHE_REMOTE_MODEL> --endpoint <TGI_SERVICE_ENDPOINT>python3 server_tgi.py --model <LOCAL_MODEL_PATH> --endpoint <TGI_SERVICE_ENDPOINT>- Make either OpenAI-compatible or raw HTTP requests to the Functionary TGI server.
Docker
If you're having trouble with dependencies, and you have nvidia-container-toolkit, you can start your environment like this:
sudo docker run --gpus all -it --ipc=host --name functionary -v ${PWD}/functionary_workspace:/workspace -p 8000:8000 nvcr.io/nvidia/pytorch:23.10-py3from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
client.chat.completions.create(
model="meetkai/functionary-small-v2.5",
messages=[{"role": "user",
"content": "What is the weather for Istanbul?"}
],
tools=[{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}],
tool_choice="auto"
)Details (click to expand)
import requests
data = {
'model': 'meetkai/functionary-small-v2.5', # model name here is the value of argument "--model" in deploying: server_vllm.py or server.py
'messages': [
{
"role": "user",
"content": "What is the weather for Istanbul?"
}
],
'tools':[ # For functionary-7b-v2 we use "tools"; for functionary-7b-v1.4 we use "functions" = [{"name": "get_current_weather", "description":..., "parameters": ....}]
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
}
response = requests.post("http://127.0.0.1:8000/v1/chat/completions", json=data, headers={
"Content-Type": "application/json",
"Authorization": "Bearer xxxx"
})
# Print the response text
print(response.text)| Model | Description | VRAM FP16 |
|---|---|---|
| functionary-medium-v3.0 / GGUF | 8k context, based on meta-llama/Meta-Llama-3-70B-Instruct | 160GB |
| functionary-small-v2.5 / GGUF | 8k context, code interpreter | 24GB |
| functionary-small-v2.4 / GGUF | 8k context, code interpreter | 24GB |
| functionary-medium-v2.4 / GGUF | 8k context, code interpreter, better accuracy | 90GB |
| functionary-small-v2.2 / GGUF | 8k context | 24GB |
| functionary-medium-v2.2 / GGUF | 8k context | 90GB |
| functionary-7b-v2.1 / GGUF | 8k context | 24GB |
| functionary-7b-v2 / GGUF | Parallel function call support. | 24GB |
| functionary-7b-v1.4 / GGUF | 4k context, better accuracy (deprecated) | 24GB |
| functionary-7b-v1.1 | 4k context (deprecated) | 24GB |
| functionary-7b-v0.1 | 2k context (deprecated) Not recommended, use 2.1 onwards | 24GB |
- v1 models are compatible with both OpenAI-python v0 and v1.
- v2 models are designed for compatibility with OpenAI-python v1.
The difference between OpenAI-python v0 and v1 you may refer to the official documentation here
| Feature/Project | Functionary | NexusRaven | Gorilla | Glaive | GPT-4-1106-preview |
|---|---|---|---|---|---|
| Single Function Call | ✅ | ✅ | ✅ | ✅ | ✅ |
| Parallel Function Calls | ✅ | ✅ | ✅ | ❌ | ✅ |
| Following Up on Missing Function Arguments | ✅ | ❌ | ❌ | ❌ | ✅ |
| Multi-turn | ✅ | ❌ | ❌ | ✅ | ✅ |
| Generate Model Responses Grounded in Tools Execution Results | ✅ | ❌ | ❌ | ❌ | ✅ |
| Chit-Chat | ✅ | ❌ | ✅ | ✅ | ✅ |
| Code Interpreter | ✅ | ❌ | ❌ | ❌ | ✅ |
You can find more details of the features in here
Example for inference using LLama-cpp-python can be found in: llama_cpp_inference.py.
Besides, functionary was also integrated into LLama-cpp-python, however the integration might not be quickly updated, so if there is something wrong or weird in the result, please use: llama_cpp_inference.py instead. Currently, v2.5 hasn't been integrated, so if you are using functionary-small-v2.5-GGUF, please use: llama_cpp_inference.py
Make sure that the latest version of llama-cpp-python is successully installed in your system. Functionary v2 is fully integrated into llama-cpp-python. You can perform inference using Functionary's GGUF models either via normal chat completion or through llama-cpp-python's OpenAI-compatible server which behaves similarly to ours.
The following is the sample code using normal chat completion:
from llama_cpp import Llama
from llama_cpp.llama_tokenizer import LlamaHFTokenizer
# We should use HF AutoTokenizer instead of llama.cpp's tokenizer because we found that Llama.cpp's tokenizer doesn't give the same result as that from Huggingface. The reason might be in the training, we added new tokens to the tokenizer and Llama.cpp doesn't handle this successfully
llm = Llama.from_pretrained(
repo_id="meetkai/functionary-small-v2.4-GGUF",
filename="functionary-small-v2.4.Q4_0.gguf",
chat_format="functionary-v2",
tokenizer=LlamaHFTokenizer.from_pretrained("meetkai/functionary-small-v2.4-GGUF"),
n_gpu_layers=-1
)
messages = [
{"role": "user", "content": "what's the weather like in Hanoi?"}
]
tools = [ # For functionary-7b-v2 we use "tools"; for functionary-7b-v1.4 we use "functions" = [{"name": "get_current_weather", "description":..., "parameters": ....}]
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g., San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
result = llm.create_chat_completion(
messages = messages,
tools=tools,
tool_choice="auto",
)
print(result["choices"][0]["message"])The output would be:
{'role': 'assistant', 'content': None, 'tool_calls': [{'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{\n "location": "Hanoi"\n}'}}]}For more details, please refer to the Function Calling section in llama-cpp-python. To use our Functionary GGUF models using llama-cpp-python's OpenAI-compatible server, please refer to here for more details and documentation.
Note:
- For Functionary in llama-cpp-python, the default system messages are added automatically during the API call. Therefore, there is no need to provide the default system messages in
messages. - Streaming feature for Functionary models in both the normal chat completion and in llama-cpp-python's OpenAI-compatible server is officially supported from v0.2.70 onwards.
To call the real python function, get the result and extract the result to respond, you can use chatlab. The following example uses chatlab==0.16.0:
Please note that Chatlab currently doesn't support Parallel Function calls. This sample code is compatible only with Functionary Version 1.4 and may not work correctly with Functionary Version 2.0.
from chatlab import Conversation
import openai
import os
openai.api_key = "functionary" # We just need to set this something other than None
os.environ['OPENAI_API_KEY'] = "functionary" # chatlab requires us to set this too
openai.api_base = "http://localhost:8000/v1"
# now provide the function with description
def get_car_price(car_name: str):
"""this function is used to get the price of the car given the name
:param car_name: name of the car to get the price
"""
car_price = {
"tang": {"price": "$20000"},
"song": {"price": "$25000"}
}
for key in car_price:
if key in car_name.lower():
return {"price": car_price[key]}
return {"price": "unknown"}
chat = Conversation(model="meetkai/functionary-7b-v2")
chat.register(get_car_price) # register this function
chat.submit("what is the price of the car named Tang?") # submit user prompt
# print the flow
for message in chat.messages:
role = message["role"].upper()
if "function_call" in message:
func_name = message["function_call"]["name"]
func_param = message["function_call"]["arguments"]
print(f"{role}: call function: {func_name}, arguments:{func_param}")
else:
content = message["content"]
print(f"{role}: {content}")The output will look like this:
USER: what is the price of the car named Tang?
ASSISTANT: call function: get_car_price, arguments:{
"car_name": "Tang"
}
FUNCTION: {'price': {'price': '$20000'}}
ASSISTANT: The price of the car named Tang is $20,000.
Serverless deployment of Functionary models is supported via the modal_server_vllm.py script. After signing up and installing Modal, follow these steps to deploy our vLLM server on Modal:
- Create dev environment
modal environment create devIf you have a dev environment created already, there is no need to create another one. Just configure to it in the next step.
- Configure dev environment
modal config set-environment dev- Serve Functionary Model
modal serve modal_server_vllm- Deploy Runner
modal deploy modal_server_vllmHere are a few examples of how you can use this function calling system:
The function plan_trip(destination: string, duration: int, interests: list) can take user input such as "I want to plan a 7-day trip to Paris with a focus on art and culture" and generate an itinerary accordingly.
Details (click to expand)
client.chat.completions.create((
model="meetkai/functionary-7b-v2",
messages=[
{"role": "user", "content": 'I want to plan a 7-day trip to Paris with a focus on art and culture'},
],
tools=[
{
"type": "function",
"function": {
"name": "plan_trip",
"description": "Plan a trip based on user's interests",
"parameters": {
"type": "object",
"properties": {
"destination": {
"type": "string",
"description": "The destination of the trip",
},
"duration": {
"type": "integer",
"description": "The duration of the trip in days",
},
"interests": {
"type": "array",
"items": {"type": "string"},
"description": "The interests based on which the trip will be planned",
},
},
"required": ["destination", "duration", "interests"],
}
}
}
]
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "plan_trip", "arguments": '{\n "destination": "Paris",\n "duration": 7,\n "interests": ["art", "culture"]\n}'}}]}Then you need to call plan_trip function with provided arguments.
If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
A function like estimate_property_value(property_details: dict) could allow users to input details about a property (such as location, size, number of rooms, etc.) and receive an estimated market value.
Details (click to expand)
client.chat.completions.create(
model="meetkai/functionary-7b-v2",
messages=[
{
"role": "user",
"content": 'What is the estimated value of a 3-bedroom house in San Francisco with 2000 sq ft area?'
},
{
"role": "assistant",
"content": None,
"tool_calls": [
{
"type": "function",
"function": {
"name": "estimate_property_value",
"arguments": '{\n "property_details": {"location": "San Francisco", "size": 2000, "rooms": 3}\n}'
}
}
]
}
],
tools=[
{
"type": "function",
"function": {
"name": "estimate_property_value",
"description": "Estimate the market value of a property",
"parameters": {
"type": "object",
"properties": {
"property_details": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The location of the property"
},
"size": {
"type": "integer",
"description": "The size of the property in square feet"
},
"rooms": {
"type": "integer",
"description": "The number of rooms in the property"
}
},
"required": ["location", "size", "rooms"]
}
},
"required": ["property_details"]
}
}
}
],
tool_choice="auto"
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "plan_trip", "arguments": '{\n "destination": "Paris",\n "duration": 7,\n "interests": ["art", "culture"]\n}'}}]}Then you need to call plan_trip function with provided arguments.
If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
A function parse_customer_complaint(complaint: {issue: string, frequency: string, duration: string}) could help in extracting structured information from a complex, narrative customer complaint, identifying the core issue and potential solutions. The complaint object could include properties such as issue (the main problem), frequency (how often the issue occurs), and duration (how long the issue has been occurring).
Details (click to expand)
client.chat.completions.create(
model="meetkai/functionary-7b-v2",
messages=[
{"role": "user", "content": 'My internet has been disconnecting frequently for the past week'},
],
tools=[
{
"type": "function",
"function": {
"name": "parse_customer_complaint",
"description": "Parse a customer complaint and identify the core issue",
"parameters": {
"type": "object",
"properties": {
"complaint": {
"type": "object",
"properties": {
"issue": {
"type": "string",
"description": "The main problem",
},
"frequency": {
"type": "string",
"description": "How often the issue occurs",
},
"duration": {
"type": "string",
"description": "How long the issue has been occurring",
},
},
"required": ["issue", "frequency", "duration"],
},
},
"required": ["complaint"],
}
}
}
],
tool_choice="auto"
)Response will have:
{"role": "assistant", "content": null, "tool_calls": [{"type": "function", "function": {"name": "parse_customer_complaint", "arguments": '{\n "complaint": {"issue": "internet disconnecting", "frequency": "frequently", "duration": "past week"}\n}'}}]}Then you need to call parse_customer_complaint function with provided arguments. If you would like a commentary from the model, then you'll call the model again with the response from the function, the model will write necessary commentary.
We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
The prompt example can be found here: V1 (v1.4), V2 (v2, v2.1, v2.2, v2.4) and V2.llama3 (v2.5)
We don't change the logit probabilities to conform to a certain schema, but the model itself knows how to conform. This allows us to use existing tools and caching systems with ease.
Evaluation function call prediction in SGD dataset. The accuracy metric measures the overall correctness of predicted function calls, including function name prediction and arguments extraction.
| Dataset | Model Name | Function Calling Accuracy (Name & Arguments) |
|---|---|---|
| SGD | MeetKai-functionary-medium-v3.0 | 89.6% |
| SGD | gpt-4o-2024-05-13 | 82.75% |
| SGD | gemini-1.5-flash | 79.64% |
| SGD | c4ai-command-r-plus | 45.66% |
See training README
- OpenAPI specification based plugin support.
- Fast inference server
- vLLM
- text-generation-inference ? See: License Issue
- Streaming Support
- function_call parameter to server
- Grammar Sampling to ensure 100% accuracy for function and parameter names
- Parallel function calling support
- Python function calling support (Automatic detection of type annotations and calling them automatically)
- Real world usage examples, such as creating agents.
- Train Mixtral based model
- Code interpreter support
- Please consider opening a PR for future requests