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README.md

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 license: apache-2.0
 ---
 
+![HelixNet](https://huggingface.co/migtissera/HelixNet/resolve/main/HelixNet.png)
+
 # HelixNet
 
 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.
@@ -30,7 +32,7 @@ Using the above training dataset, a Mistral-7B was fine-tuned.
 A thrid LLM was fine-tuned using the above data.
 
 
-# Reusability of the `critic` and the `regenerator`
+# Reusability of the critic and the regenerator
 
 The `critic` and the `regenerator` was tested not only on the accopanying actor model, but 13B and 70B SynthIA models as well. They seem to be readily transferrable, as the function that it has learnt is to provide an intelligent critique and then a regeneration of the original response. Please feel free to try out other models as the `actor`. However, the architecture works best with all three as presented here in HelixNet.