functionary
Chat language model that can use tools and interpret the results
Stars: 1382
Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.
README:
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)
- [2024-08-11] Our newest model (meetkai/functionary-medium-v3.1) is ranked 2nd in Berkeley Function-Calling Leaderboard
- [2024/08/08] We release 128k-context length 70B-model: meetkai/functionary-medium-v3.1 that are based on meta-llama/Meta-Llama-3.1-70B-Instruct
- [2024/08/07] We release 2 128k-context length models that are based on meta-llama/Meta-Llama-3.1-8B-Instruct:
- meetkai/functionary-small-v3.1: using Meta's original prompt template as described in: User-defined Custom tool calling
- meetkai/functionary-small-v3.2: using our own prompt template. This model is better than meetkai/functionary-small-v3.1
- [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.txt
Now you can start a blazing fast vLLM server. requirements
Small Model:
python3 server_vllm.py --model "meetkai/functionary-small-v3.2" --host 0.0.0.0 --max-model-len 8192
Medium Model:
Our medium models require: 4xA6000 or 2xA100 80GB to run, need to use: tensor-parallel-size
# vllm requires to run this first: https://github.com/vllm-project/vllm/issues/6152
export VLLM_WORKER_MULTIPROC_METHOD=spawn
python server_vllm.py --model "meetkai/functionary-medium-v3.1" --max-model-len 8192 --tensor-parallel-size 2
Grammar 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-v3.1" --max-model-len 8192 --tensor-parallel-size 2 --enable-grammar-sampling
Note:
- Grammar Sampling support is applicable only for the V2 and V3.0 models. There is no such support for V1 and V3.1 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-py3
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
client.chat.completions.create(
model="meetkai/functionary-small-v3.2",
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-v3.2', # 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-small-v3.2 / GGUF | 128k context, code interpreter, using our own prompt template | 24GB |
functionary-medium-v3.1 / GGUF | 128k context, code interpreter, using original Meta's prompt template | 160GB |
functionary-small-v3.1 / GGUF | 128k context, code interpreter, using original Meta's prompt template | 24GB |
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 dev
If 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_vllm
Here 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.
We are ranked 2nd in the Berkeley Function-Calling Leaderboard (Last Updated: 2024-08-11)
Model Name | Function Calling Accuracy (Name & Arguments) |
---|---|
meetkai/functionary-medium-v3.1 | 88.88% |
GPT-4-1106-Preview (Prompt) | 88.53% |
meetkai/functionary-small-v3.2 | 82.82% |
meetkai/functionary-small-v3.1 | 82.53% |
FireFunction-v2 (FC) | 78.82.47% |
We also evaluate our models on ToolSandbox, this benchmark is much more difficult than Berkeley Function-Calling Leaderboard. This benchmark includes stateful tool execution, implicit state dependencies between tools, a built-in user simulator supporting on-policy conversational evaluation and a dynamic evaluation strategy for intermediate and final milestones over an arbitrary trajectory. The authors of this benchmark showed that there is a huge performance gap between open source models and proprietary models.
From our evaluation result, our models are comparable to best proprietary models and much better than other open source models.
Model Name | Average similarity score |
---|---|
GPT-4o-2024-05-13 | 73 |
Claude-3-Opus-20240229 | 69.2 |
Functionary-medium-v3.1 | 68.87 |
GPT-3.5-Turbo-0125 | 65.6 |
GPT-4-0125-Preview | 64.3 |
Claude-3-Sonnet-20240229 | 63.8 |
Functionary-small-v3.1 | 63.13 |
Gemini-1.5-Pro-001 | 60.4 |
Functionary-small-v3.2 | 58.56 |
Claude-3-Haiku-20240307 | 54.9 |
Gemini-1.0-Pro | 38.1 |
Hermes-2-Pro-Mistral-7B | 31.4 |
Mistral-7B-Instruct-v0.3 | 29.8 |
C4AI-Command-R-v01 | 26.2 |
Gorilla-Openfunctions-v2 | 25.6 |
C4AI-Command R+ | 24.7 |
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-v3.1 | 88.11% |
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
While its not strictly enforced, to ensure more secure function execution, one can enable grammar sampling to enforce type checking. Main safety checks needs to be done in the functions/actions themselves. Such as validation of the given input, or the ouput that will be given to the model.
- [ ] OpenAPI specification based plugin support.
- [X] Fast inference server
- [X] vLLM
- [ ] text-generation-inference ? See: License Issue
- [X] Streaming Support
- [X] function_call parameter to server
- [X] Grammar Sampling to ensure 100% accuracy for function and parameter names
- [X] Parallel function calling support
- [X] Python function calling support (Automatic detection of type annotations and calling them automatically)
- [X] Real world usage examples, such as creating agents.
- [X] Train Mixtral based model
- [X] Code interpreter support
- Please consider opening a PR for future requests
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for functionary
Similar Open Source Tools
functionary
Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.
lego-ai-parser
Lego AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements. It is built on top of FastAPI, ready to set up as a server, and make calls from any language. It supports preset parsers for Google Local Results, Amazon Listings, Etsy Listings, Wayfair Listings, BestBuy Listings, Costco Listings, Macy's Listings, and Nordstrom Listings. Users can also design custom parsers by providing prompts, examples, and details about the OpenAI model under the classifier key.
langcorn
LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience. It offers features such as easy deployment of LangChain models and pipelines, ready-to-use authentication functionality, high-performance FastAPI framework for serving requests, scalability and robustness for language processing applications, support for custom pipelines and processing, well-documented RESTful API endpoints, and asynchronous processing for faster response times.
agentic_security
Agentic Security is an open-source vulnerability scanner designed for safety scanning, offering customizable rule sets and agent-based attacks. It provides comprehensive fuzzing for any LLMs, LLM API integration, and stress testing with a wide range of fuzzing and attack techniques. The tool is not a foolproof solution but aims to enhance security measures against potential threats. It offers installation via pip and supports quick start commands for easy setup. Users can utilize the tool for LLM integration, adding custom datasets, running CI checks, extending dataset collections, and dynamic datasets with mutations. The tool also includes a probe endpoint for integration testing. The roadmap includes expanding dataset variety, introducing new attack vectors, developing an attacker LLM, and integrating OWASP Top 10 classification.
AICentral
AI Central is a powerful tool designed to take control of your AI services with minimal overhead. It is built on Asp.Net Core and dotnet 8, offering fast web-server performance. The tool enables advanced Azure APIm scenarios, PII stripping logging to Cosmos DB, token metrics through Open Telemetry, and intelligent routing features. AI Central supports various endpoint selection strategies, proxying asynchronous requests, custom OAuth2 authorization, circuit breakers, rate limiting, and extensibility through plugins. It provides an extensibility model for easy plugin development and offers enriched telemetry and logging capabilities for monitoring and insights.
AIGODLIKE-ComfyUI-Translation
A plugin for multilingual translation of ComfyUI, This plugin implements translation of resident menu bar/search bar/right-click context menu/node, etc
llama.rn
React Native binding of llama.cpp, which is an inference of LLaMA model in pure C/C++. This tool allows you to use the LLaMA model in your React Native applications for various tasks such as text completion, tokenization, detokenization, and embedding. It provides a convenient interface to interact with the LLaMA model and supports features like grammar sampling and mocking for testing purposes.
mergoo
Mergoo is a library for easily merging multiple LLM experts and efficiently training the merged LLM. With Mergoo, you can efficiently integrate the knowledge of different generic or domain-based LLM experts. Mergoo supports several merging methods, including Mixture-of-Experts, Mixture-of-Adapters, and Layer-wise merging. It also supports various base models, including LLaMa, Mistral, and BERT, and trainers, including Hugging Face Trainer, SFTrainer, and PEFT. Mergoo provides flexible merging for each layer and supports training choices such as only routing MoE layers or fully fine-tuning the merged LLM.
chatgpt-exporter
A script to export the chat history of ChatGPT. Supports exporting to text, HTML, Markdown, PNG, and JSON formats. Also allows for exporting multiple conversations at once.
UHGEval
UHGEval is a comprehensive framework designed for evaluating the hallucination phenomena. It includes UHGEval, a framework for evaluating hallucination, XinhuaHallucinations dataset, and UHGEval-dataset pipeline for creating XinhuaHallucinations. The framework offers flexibility and extensibility for evaluating common hallucination tasks, supporting various models and datasets. Researchers can use the open-source pipeline to create customized datasets. Supported tasks include QA, dialogue, summarization, and multi-choice tasks.
crawl4ai
Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.
scylla
Scylla is an intelligent proxy pool tool designed for humanities, enabling users to extract content from the internet and build their own Large Language Models in the AI era. It features automatic proxy IP crawling and validation, an easy-to-use JSON API, a simple web-based user interface, HTTP forward proxy server, Scrapy and requests integration, and headless browser crawling. Users can start using Scylla with just one command, making it a versatile tool for various web scraping and content extraction tasks.
Webscout
WebScout is a versatile tool that allows users to search for anything using Google, DuckDuckGo, and phind.com. It contains AI models, can transcribe YouTube videos, generate temporary email and phone numbers, has TTS support, webai (terminal GPT and open interpreter), and offline LLMs. It also supports features like weather forecasting, YT video downloading, temp mail and number generation, text-to-speech, advanced web searches, and more.
blendsql
BlendSQL is a superset of SQLite designed for problem decomposition and hybrid question-answering with Large Language Models (LLMs). It allows users to blend operations over heterogeneous data sources like tables, text, and images, combining the structured and interpretable reasoning of SQL with the generalizable reasoning of LLMs. Users can oversee all calls (LLM + SQL) within a unified query language, enabling tasks such as building LLM chatbots for travel planning and answering complex questions by injecting 'ingredients' as callable functions.
Scrapegraph-ai
ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.
llmproxy
llmproxy is a reverse proxy for LLM API based on Cloudflare Worker, supporting platforms like OpenAI, Gemini, and Groq. The interface is compatible with the OpenAI API specification and can be directly accessed using the OpenAI SDK. It provides a convenient way to interact with various AI platforms through a unified API endpoint, enabling seamless integration and usage in different applications.
For similar tasks
functionary
Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.
For similar jobs
weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.
spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.