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MixtureOfAgentsLLM

Mixture-of-Agents implementation.

An LLM class that leverages LLMs collective strenghts to generate a response, as described in the "Mixture-of-Agents Enhances Large Language model Capabilities" paper. There is a list of LLMs proposing/generating outputs that LLMs from the next round/layer can use as auxiliary information. Finally, there is an LLM that aggregates the outputs to generate the final response.

Attributes

  • aggregator_llm: The LLM that aggregates the outputs of the proposer LLMs.

  • proposers_llms: The list of LLMs that propose outputs to be aggregated.

  • rounds: The number of layers or rounds that the proposers_llms will generate outputs. Defaults to 1.

Examples

Generate text

from distilabel.llms import MixtureOfAgentsLLM, InferenceEndpointsLLM

llm = MixtureOfAgentsLLM(
    aggregator_llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
    ),
    proposers_llms=[
        InferenceEndpointsLLM(
            model_id="meta-llama/Meta-Llama-3-70B-Instruct",
            tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
        ),
        InferenceEndpointsLLM(
            model_id="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
            tokenizer_id="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        ),
        InferenceEndpointsLLM(
            model_id="HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
            tokenizer_id="HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
        ),
    ],
    rounds=2,
)

llm.load()

output = llm.generate_outputs(
    inputs=[
        [
            {
                "role": "user",
                "content": "My favorite witty review of The Rings of Power series is this: Input:",
            }
        ]
    ]
)

References