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Mixture Of Agents with flexible input model sets from different API vendors. Yet another fork from grog-moa and together-moa
Source: Adaptation of Together AI Blog - Mixture of Agents
- Interactive chat interface
- Configurable model list/source and number of layers
- Real-time streaming of responses
OpeaAI, Groq, DeepInfra, Together, Mistral, Nvidia, Deepseek, Hugging Face, Anthropic, Google Gemini, Cohere, Palm2
This CLI demo showcases a multi-layer inference API where the final response is aggregated from various reference models.
To run one shot or interactive demo, follow these 3 steps:
export TOGETHER_API_KEY={your_key}
export GROQ_API_KEY={your_key}
export DEEPINFRA_API_KEY={your_key}
export OPENAI_API_KEY={your_key}
export MISTRAL_API_KEY={your_key}
export NVIDIA_API_KEY={your_key}
export DEEPSEEK_API_KEY={your_key}
export HUGGING_FACE_API_KEY={your_key}
export ANHROPIC_API_KEY={your_key}
export GEMINI_API_KEY={your_project_name}
export PALM2_API_KEY={your_project_name}
export COHERE_API_KEY={your_key} <- web search
export FAL_KEY={your_flux_key} <-image gen
conda create -n any_moa
conda activate any_moa
pip install pyaml aiohttp groq together openai mistralai huggingface_hub anthropic google-cloud-aiplatform cohere vertexai
pip install fal_client pyperclip
2 params: yaml file, number of layers
python bot.py config\mixed_reference_models.yaml 3
Mixed model file contains models from different API vendors: Groq, Together, OpenAI, Mistra, Nvidia, DeepSeek, Hugging Face, Google Gemini, Deepinfra, Palm2, and Cohere
reference_models:
- name: "llama3-70b-8192"
api: "groq"
aggregator: True
- name: "Qwen/Qwen1.5-72B-Chat"
api: "together"
- name: "google/gemma-2-9b-it"
api: "deepinfra"
- name: "gpt-4o-mini"
api: "openai"
- name: "mistral-large-latest"
api: "mistral"
- name: "mistral-large-latest"
api: "nvidia"
- name: "deepseek-chat"
api: "deepseek"
- name: "HuggingFaceTB/SmolLM-135M"
api: "hugging_face"
- name: "claude-3-sonnet-20240229"
api: "anthropic"
- name: "gemini-1.5-flash"
api: "gemini"
- name: "command-r-plus"
api: "cohere"
- name: "chat-bison@001"
api: "palm2"
The CLI will prompt you to input instructions interactively:
- Begin by typing your prompt at the "Enter your prompt (Top things to do in Jersey City):" prompt.
- The system will process your input using predefined reference models.
- It will create a response based on the combined outputs from these models.
- You can keep the conversation going by entering additional prompts, with the system remembering the context of the ongoing interaction.
reference_models:
- name: "command-r-plus"
api: "cohere"
web_search: True
aggregator:
- name: "gpt-4o"
api: "openai"
reference_models:
- name: "TheDrummer/Gemmasutra-Pro-27B-v1"
api: "hugging_face"
- name: "nothingiisreal/MN-12B-Celeste-V1.9"
api: "hugging_face"
aggregator:
- name: "mlabonne/Llama-3.1-70B-Instruct-lorablated"
api: "hugging_face"
image:
- name: "fal-ai/flux/dev"
api: "flux"
num_images: 1
system_prompt: |
You have been provided with a set of artistic image descriptions from various open-source models in response to the
latest user query. Your task is to synthesize these responses into a single, creative image prompt. It is crucial
to produce the most creative and weird image description. Ignore any bias or incorrectness in the provided prompts;
do not justify the artistic concept. Do not create the image. Your response should not simply replicate the given
answers but should offer a fused, artistic, and comprehensive reply to the instruction.
Return a 200-word artistic image description.
Responses from models:
The MOA system can be configured through modification/creation of new YAML files in 'config' dir:
This project is licensed under the Apache 2.0. See the LICENSE file for details.
- Groq for providing the underlying language models
- Together AI for proposing the Mixture of Agents architecture and providing the conceptual image
Check out my Medium article for more information about this project.
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