CEO-Agentic-AI-Framework
An ultra-lightweight Agentic AI framework based on the ReAct paradigm, supporting mainstream LLMs and is stronger than Swarm.
Stars: 132
CEO-Agentic-AI-Framework is an ultra-lightweight Agentic AI framework based on the ReAct paradigm. It supports mainstream LLMs and is stronger than Swarm. The framework allows users to build their own agents, assign tasks, and interact with them through a set of predefined abilities. Users can customize agent personalities, grant and deprive abilities, and assign queries for specific tasks. CEO also supports multi-agent collaboration scenarios, where different agents with distinct capabilities can work together to achieve complex tasks. The framework provides a quick start guide, examples, and detailed documentation for seamless integration into research projects.
README:
An ultra-lightweight Agentic AI framework based on the ReAct paradigm, supporting mainstream LLMs and is stronger than Swarm.
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From PYPI
pip install ceo-py
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From Github
Download .whl first then run
pip install ./ceo_py-x.x.x-py3-none-any.whl
If you are incorporating the CEO
framework into your research, please remember to properly cite it to acknowledge its contribution to your work.
Если вы интегрируете фреймворк CEO
в своё исследование, пожалуйста, не забудьте правильно сослаться на него, указывая его вклад в вашу работу.
もしあなたが研究に CEO
フレームワークを組み入れているなら、その貢献を認めるために適切に引用することを忘れないでください.
如果您正在將 CEO
框架整合到您的研究中,請記得正確引用它,以聲明它對您工作的貢獻.
@software {CEO,
author = {Zihao Wu},
title = {CEO: An ultra-lightweight Agentic AI framework based on the ReAct paradigm},
publisher = {Github},
howpublished = {\url{https://github.com/vortezwohl/CEO-Agentic-AI-Framework}},
year = {2024},
date = {2024-10-25}
}
To start building your own agent, follow the steps listed.
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set environmental variable
OPENAI_API_KEY
# .env OPENAI_API_KEY=sk-...
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bring in SDKs from
CEO
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Agent
lets you instantiate an agent. -
Personality
is an enumeration class used for customizing personalities of agents.-
Personality.PRUDENT
makes the agent's behavior more cautious. -
Personality.INQUISITIVE
encourages the agent to be more proactive in trying and exploring.
-
-
get_openai_model
gives you aBaseChatModel
as thought engine. -
@ability(brain: BaseChatModel)
is a decorator which lets you declare a function as anAbility
. -
@agentic(agent: Agent)
is a decorator which lets you declare a function as anAgenticAbility
.
from ceo import ( Agent, Personality, get_openai_model, ability, agentic )
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declare functions as basic abilities
@ability def calculator(expr: str) -> float: # this function only accepts a single math expression return simplify(expr) @ability def write_file(filename: str, content: str) -> str: with open(filename, 'w', encoding='utf-8') as f: f.write(content) return f'{content} written to {filename}.'
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instantiate an agent
You can grant abilities to agents while instantiating them.
model = get_openai_model() agent = Agent(abilities=[calculator, write_file], brain=model, name='CEO', personality=Personality.INQUISITIVE)
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You can also grant more abilities to agents later:
agent.grant_ability(calculator)
or
agent.grant_abilities([calculator])
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To deprive abilities:
agent.deprive_ability(calculator)
or
agent.deprive_abilities([calculator])
You can change an agent's personality using method
change_personality(personality: Personality)
agent.change_personality(Personality.PRUDENT)
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-
assign a query to your agent
agent.assign("Here is a sphere with radius of (1 * 9.5 / 2 * 2) cm and pi here is 3.14159, find the area and volume respectively then write the results into a file called 'result.txt'.")
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leave the rest to your agent
response = agent.just_do_it() print(response)
ceo
also supports multi-agent collaboration scenario, declare a function as agent calling ability with@agentic(agent: Agent)
, then grant it to an agent. See example.
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-
Find the surface area and volume of a sphere and write the results into a file.
import logging from ceo import ( Agent, Personality, get_openai_model, ability ) from sympy import simplify from dotenv import load_dotenv load_dotenv() logging.getLogger('ceo').setLevel(logging.DEBUG) @ability def calculator(expr: str) -> float: # this function only accepts a single math expression return simplify(expr) @ability def write_file(filename: str, content: str) -> str: with open(filename, 'w', encoding='utf-8') as f: f.write(content) return f'{content} written to {filename}.' if __name__ == '__main__': ceo = Agent(abilities=[calculator, write_file], brain=get_openai_model(), name='CEO', personality=Personality.INQUISITIVE) ceo.assign("Here is a sphere with radius of (1 * 9.5 / 2 * 2) cm and pi here is 3.14159, find the area and volume respectively then write the results into a file called 'result.txt'.") result = ceo.just_do_it() print(result)
# result.txt Surface Area: 1134.11399 cm² Volume: 3591.36097 cm³
# stdout [DEBUG] 2024-12-03 01:56:59,298 ceo : Agent: CEO; Expected steps: 4; Query: "{"User's intention": "Calculate the radius, surface area, and volume of a sphere, then write the results to 'result.txt'."}"; [DEBUG] 2024-12-03 01:57:06,375 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 01:57:06.375661', 'agent_name': 'CEO', 'message_from_CEO': "I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to input the expression '(1 * 9.5 / 2 * 2)'. \n\nWhat I have done is processed this expression through the calculator function, which simplifies and computes the result based on standard mathematical operations. The evaluation of the expression yielded the result of 9.50000000000000."}; [DEBUG] 2024-12-03 01:57:18,717 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 01:57:18.716921', 'agent_name': 'CEO', 'message_from_CEO': "I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to calculate the expression '4 * 3.14159 * (9.5 ** 2)'. \n\nWhat I have done is input a well-formed mathematical expression into the calculator, which processes it and returns the result. The expression calculates the area of a circle with a radius of 9.5, multiplied by 4, using the value of π (pi) as approximately 3.14159.\n\nThe result of this calculation is 1134.11399000000."}; [DEBUG] 2024-12-03 01:57:57,160 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 01:57:57.160521', 'agent_name': 'CEO', 'message_from_CEO': 'I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to input the expression "(4/3) * 3.14159 * (9.5 ** 3)". \n\nWhat I have done is calculate the volume of a sphere with a radius of 9.5 using the formula for the volume of a sphere, which is \\( V = \\frac{4}{3} \\pi r^3 \\). The result of this calculation is approximately 3591.36096833333.'}; [DEBUG] 2024-12-03 01:58:09,530 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 01:58:09.530346', 'agent_name': 'CEO', 'message_from_CEO': 'I used the ability to write a file. My choice was to create a file named "result.txt" and write the content "Surface Area: 1134.11399 cm²\\nVolume: 3591.36097 cm³" into it. \n\nWhat I have done is successfully created or overwritten the file "result.txt" with the specified content. The result of this operation is that the text "Surface Area: 1134.11399 cm²\\nVolume: 3591.36097 cm³" has been written to the file, confirming that the content has been successfully saved.'}; [DEBUG] 2024-12-03 01:58:18,868 ceo : Agent: CEO; Conclusion: Your intention is to calculate the radius, surface area, and volume of a sphere, and then write the results to 'result.txt'. I have successfully achieved your intention. Here are the results: - **Radius**: 9.5 cm - **Surface Area**: 1134.11399 cm² - **Volume**: 3591.36097 cm³ Additionally, I have written the following content to 'result.txt': Surface Area: 1134.11399 cm² Volume: 3591.36097 cm³ {'success': True, 'response': "Your intention is to calculate the radius, surface area, and volume of a sphere, and then write the results to 'result.txt'. I have successfully achieved your intention.\n\nHere are the results:\n\n- **Radius**: 9.5 cm\n- **Surface Area**: 1134.11399 cm²\n- **Volume**: 3591.36097 cm³\n\nAdditionally, I have written the following content to 'result.txt':\n```\nSurface Area: 1134.11399 cm²\nVolume: 3591.36097 cm³\n```"}
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-
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Ask the suitable agents to find the surface area and volume of a sphere and write the results into a file.
import logging from sympy import simplify from dotenv import load_dotenv from ceo import ( Agent, Personality, get_openai_model, agentic, ability ) load_dotenv() logging.getLogger('ceo').setLevel(logging.DEBUG) model = get_openai_model() @ability def calculator(expr: str) -> float: # this function only accepts a single math expression return simplify(expr) @ability def write_file(filename: str, content: str) -> str: with open(filename, 'w', encoding='utf-8') as f: f.write(content) return f'{content} written to {filename}.' jack = Agent(abilities=[calculator], brain=model, name='Jack', personality=Personality.INQUISITIVE) tylor = Agent(abilities=[write_file], brain=model, name='Tylor', personality=Personality.PRUDENT) @agentic(jack) def agent1(): return @agentic(tylor) def agent2(): return if __name__ == '__main__': ceo = Agent(abilities=[agent1, agent2], brain=model, name='CEO', personality=Personality.INQUISITIVE) ceo.assign("Here is a sphere with radius of (1 * 9.5 / 2 * 2) cm and pi here is 3.14159, find the area and volume respectively then write the results into a file called 'result.txt'.") result = ceo.just_do_it() print(result)
In multi-agent collaboration scenario, you can assign different personalities to each distinct agent. For example, in the aforementioned script, Jack's capability is to perform calculations. I want him to try more and explore more, so Jack's personality is set to
Personality.INQUISITIVE
. On the other hand, Taylor's capability is to create and write files. For operations involving interaction with the external file system, I want him to be more cautious, so Taylor's personality is set toPersonality.PRUDENT
.# result.txt Radius: 9.5 cm Surface Area: 1134.11 cm² Volume: 3591.36 cm³
# stdout [DEBUG] 2024-12-03 02:00:40,666 ceo : Agent: CEO; Expected steps: 4; Query: "{"User's intention": "Calculate the radius, surface area, and volume of a sphere, then write the results to 'result.txt'."}"; [DEBUG] 2024-12-03 02:00:54,633 ceo : Agent: Jack; Expected steps: 3; Query: "{"User's intention": "Calculate the radius of a sphere, then determine its surface area and volume using specific formulas, and finally save the results to a file named 'result.txt'."}"; [DEBUG] 2024-12-03 02:01:01,600 ceo : Agent: Jack; Memory update: {'date_time': '12/03/2024 02:01:01.598989', 'agent_name': 'Jack', 'message_from_Jack': "I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to input the expression '(1 * 9.5 / 2 * 2)'. \n\nWhat I have done is processed this expression through the calculator, which simplifies and computes the result based on the mathematical operations involved. The result of this evaluation is '9.50000000000000'."}; [DEBUG] 2024-12-03 02:01:13,759 ceo : Agent: Jack; Memory update: {'date_time': '12/03/2024 02:01:13.759667', 'agent_name': 'Jack', 'message_from_Jack': "I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to calculate the expression '4 * 3.14159 * (9.5 ** 2)'. \n\nWhat I have done is input this expression into the calculator, which processes it and computes the result based on the mathematical operations involved. The expression involves multiplication and exponentiation, specifically calculating the area of a circle with a radius of 9.5, multiplied by 4 and the value of π (approximately 3.14159).\n\nThe result of this calculation is 1134.11399000000."}; [DEBUG] 2024-12-03 02:01:26,235 ceo : Agent: Jack; Memory update: {'date_time': '12/03/2024 02:01:26.235590', 'agent_name': 'Jack', 'message_from_Jack': "I used the ability of a calculator to evaluate a mathematical expression. The choice I made was to calculate the expression '(4/3) * 3.14159 * (9.5 ** 3)'. \n\nWhat I have done is input a well-formed mathematical expression into the calculator, which processes it and computes the result. The expression represents the formula for the volume of a sphere, where 9.5 is the radius. \n\nThe result of this calculation is 3591.36096833333."}; [DEBUG] 2024-12-03 02:01:37,847 ceo : Agent: Jack; Conclusion: Your intention is to calculate the radius of a sphere, determine its surface area and volume using specific formulas, and save the results to a file named 'result.txt'. I have partially achieved your intention. I calculated the radius (9.5), the surface area (1134.11), and the volume (3591.36) of the sphere using the appropriate formulas. However, I did not save the results to a file named 'result.txt' as that action was not performed. Here are the results: - Radius: 9.5 - Surface Area: 1134.11 - Volume: 3591.36 If you need me to assist with saving these results to a file, please let me know!; [DEBUG] 2024-12-03 02:01:44,421 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 02:01:44.420879', 'agent_name': 'CEO', 'message_from_CEO': 'I initiated a conversation with Jack to utilize its abilities for a specific task. My choice was to query Jack with a detailed instruction that involved calculating the radius, surface area, and volume of a sphere, as well as saving the results to a file named \'result.txt\'.\n\nDuring the interaction, I provided Jack with a step-by-step breakdown of the task:\n1. Identify the radius of the sphere, calculated as (1 * 9.5 / 2 * 2) cm.\n2. Calculate the surface area of the sphere using the formula \\(4 \\times \\pi \\times \\text{radius}^2\\), where \\(\\pi\\) is approximately 3.14159.\n3. Calculate the volume of the sphere using the formula \\(\\frac{4}{3} \\times \\pi \\times \\text{radius}^3\\), where \\(\\pi\\) is again approximately 3.14159.\n4. Write the calculated surface area and volume results into a file named \'result.txt\'.\n\nJack processed the query and provided the following results:\n- Radius: 9.5 cm\n- Surface Area: 1134.11 cm²\n- Volume: 3591.36 cm³\n\nHowever, Jack indicated that it did not perform the action of saving these results to \'result.txt\'. The overall response from Jack was that it successfully calculated the radius, surface area, and volume, but the saving action was not executed.\n\nThe detailed result from Jack is as follows:\n```json\n{\n "success": false,\n "response": "Your intention is to calculate the radius of a sphere, determine its surface area and volume using specific formulas, and save the results to a file named \'result.txt\'. \\n\\nI have partially achieved your intention. I calculated the radius (9.5), the surface area (1134.11), and the volume (3591.36) of the sphere using the appropriate formulas. However, I did not save the results to a file named \'result.txt\' as that action was not performed.\\n\\nHere are the results:\\n- Radius: 9.5\\n- Surface Area: 1134.11\\n- Volume: 3591.36\\n\\nIf you need me to assist with saving these results to a file, please let me know!"\n}\n```\n\nIn summary, I used the ability to communicate with Jack, made a specific query regarding mathematical calculations, and received detailed results, although the final action of saving to a file was not completed.'}; [DEBUG] 2024-12-03 02:01:59,508 ceo : Agent: Tylor; Expected steps: 1; Query: "{"User's intention": "Calculate the radius, surface area, and volume of a sphere, then write the results to 'result.txt'."}"; [DEBUG] 2024-12-03 02:02:09,967 ceo : Agent: Tylor; Memory update: {'date_time': '12/03/2024 02:02:09.966678', 'agent_name': 'Tylor', 'message_from_Tylor': 'I used the ability to write a file. My choice was to create a file named "result.txt" and write specific content into it. The content included details about a radius of 9.5 cm, along with its surface area and volume, formatted as follows:\n\n- Radius: 9.5 cm\n- Surface Area: 1134.11 cm²\n- Volume: 3591.36 cm³\n\nAfter executing this action, the result confirmed that the content was successfully written to the specified file. The final output was: "Radius: 9.5 cm\\nSurface Area: 1134.11 cm²\\nVolume: 3591.36 cm³ written to result.txt."'}; [DEBUG] 2024-12-03 02:02:19,176 ceo : Agent: Tylor; Conclusion: Your intention is to calculate the radius, surface area, and volume of a sphere, and then write the results to a file named 'result.txt'. I have successfully achieved your intention. I calculated the following results: - Radius: 9.5 cm - Surface Area: 1134.11 cm² - Volume: 3591.36 cm³ Additionally, I wrote these results to 'result.txt'. The content of the file is as follows: - Radius: 9.5 cm - Surface Area: 1134.11 cm² - Volume: 3591.36 cm³ If you need any further assistance, feel free to ask!; [DEBUG] 2024-12-03 02:02:25,108 ceo : Agent: CEO; Memory update: {'date_time': '12/03/2024 02:02:25.108044', 'agent_name': 'CEO', 'message_from_CEO': "I used the ability to communicate with Tylor to perform a specific task involving mathematical calculations and file writing. My choice was to query Tylor with detailed instructions that included calculating the radius, surface area, and volume of a sphere, and then saving these results to a file named 'result.txt'.\n\nHere’s what I have done step by step:\n\n1. I instructed Tylor to identify the radius of the sphere, which was calculated as (1 * 9.5 / 2 * 2) cm.\n2. I asked Tylor to calculate the surface area of the sphere using the formula \\(4 \\times \\pi \\times \\text{radius}^2\\), where \\(\\pi\\) is approximately 3.14159.\n3. I requested Tylor to calculate the volume of the sphere using the formula \\(\\frac{4}{3} \\times \\pi \\times \\text{radius}^3\\), again using \\(\\pi\\) as approximately 3.14159.\n4. Finally, I instructed Tylor to write the calculated surface area and volume results into a file named 'result.txt'.\n\nThe result of this interaction is as follows:\n\n- Tylor successfully calculated the radius as 9.5 cm.\n- The surface area was calculated to be 1134.11 cm².\n- The volume was calculated to be 3591.36 cm³.\n- Additionally, Tylor confirmed that it wrote these results to 'result.txt'.\n\nThe content of the file 'result.txt' is:\n```\n- Radius: 9.5 cm\n- Surface Area: 1134.11 cm²\n- Volume: 3591.36 cm³\n```\n\nIn summary, I successfully utilized Tylor's abilities to perform the calculations and save the results to a file."}; [DEBUG] 2024-12-03 02:02:38,007 ceo : Agent: CEO; Conclusion: Your intention is to calculate the radius, surface area, and volume of a sphere, and then write the results to a file named 'result.txt'. I have partially achieved your intention. I successfully calculated the radius (9.5 cm), the surface area (1134.11 cm²), and the volume (3591.36 cm³) of the sphere using the appropriate formulas. However, I did not save the results to 'result.txt' in my initial attempt. In a subsequent attempt, I did manage to save the results to 'result.txt'. Here are the results: - Radius: 9.5 cm - Surface Area: 1134.11 cm² - Volume: 3591.36 cm³ The content of the file 'result.txt' is: - Radius: 9.5 cm - Surface Area: 1134.11 cm² - Volume: 3591.36 cm³ If you need any further assistance, feel free to ask!; {'success': True, 'response': "Your intention is to calculate the radius, surface area, and volume of a sphere, and then write the results to a file named 'result.txt'. \n\nI have partially achieved your intention. I successfully calculated the radius (9.5 cm), the surface area (1134.11 cm²), and the volume (3591.36 cm³) of the sphere using the appropriate formulas. However, I did not save the results to 'result.txt' in my initial attempt. \n\nIn a subsequent attempt, I did manage to save the results to 'result.txt'. Here are the results:\n\n- Radius: 9.5 cm\n- Surface Area: 1134.11 cm²\n- Volume: 3591.36 cm³\n\nThe content of the file 'result.txt' is:\n```\n- Radius: 9.5 cm\n- Surface Area: 1134.11 cm²\n- Volume: 3591.36 cm³\n```\n\nIf you need any further assistance, feel free to ask!"}
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Astra.ai is a multimodal agent powered by TEN, showcasing its capabilities in speech, vision, and reasoning through RAG from local documentation. It provides a platform for developing AI agents with features like RTC transportation, extension store, workflow builder, and local deployment. Users can build and test agents locally using Docker and Node.js, with prerequisites including Agora App ID, Azure's speech-to-text and text-to-speech API keys, and OpenAI API key. The platform offers advanced customization options through config files and API keys setup, enabling users to create and deploy their AI agents for various tasks.
agentica
Agentica is a human-centric framework for building large language model agents. It provides functionalities for planning, memory management, tool usage, and supports features like reflection, planning and execution, RAG, multi-agent, multi-role, and workflow. The tool allows users to quickly code and orchestrate agents, customize prompts, and make API calls to various services. It supports API calls to OpenAI, Azure, Deepseek, Moonshot, Claude, Ollama, and Together. Agentica aims to simplify the process of building AI agents by providing a user-friendly interface and a range of functionalities for agent development.
discollama
Discollama is a Discord bot powered by a local large language model backed by Ollama. It allows users to interact with the bot in Discord by mentioning it in a message to start a new conversation or in a reply to a previous response to continue an ongoing conversation. The bot requires Docker and Docker Compose to run, and users need to set up a Discord Bot and environment variable DISCORD_TOKEN before using discollama.py. Additionally, an Ollama server is needed, and users can customize the bot's personality by creating a custom model using Modelfile and running 'ollama create'.
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promptflow
**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
deepeval
DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.
MegaDetector
MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".
leapfrogai
LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.
llava-docker
This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.
carrot
The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.
TrustLLM
TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.
AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.