Migel Tissera
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README.md
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# HelixNet
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![HelixNet](https://huggingface.co/migtissera/HelixNet/resolve/main/HelixNet.png)
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HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
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# HelixNet
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![HelixNet](https://huggingface.co/migtissera/HelixNet/resolve/main/media/HelixNet.png)
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HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
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HelixNet.png → media/HelixNet.png
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