Migtissera

Models by this creator

🏷️

SynthIA-7B-v1.3

migtissera

Total Score

142

The SynthIA-7B-v1.3 is a Mistral-7B-v0.1 model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations. The model is released by migtissera under the Apache 2.0 license. Similar models include the neural-chat-7b-v3-1 and neural-chat-7b-v3-3 models, which are also fine-tuned 7B language models. However, the SynthIA-7B-v1.3 is focused on instruction following and open-ended conversations, rather than the more specialized tasks of those models. Model inputs and outputs Inputs Instruction**: The model accepts instructions or prompts for the AI assistant to elaborate on using a Tree of Thoughts and Chain of Thought reasoning. Outputs Natural language response**: The model generates a coherent, step-by-step response that addresses the given instruction or prompt. Capabilities The SynthIA-7B-v1.3 model demonstrates strong capabilities in open-ended instruction following and long-form conversation. It can break down complex topics, explore relevant sub-topics, and construct a clear reasoning to answer questions or address prompts. The model's performance is evaluated to be on par with other leading 7B language models. What can I use it for? The SynthIA-7B-v1.3 model would be well-suited for applications that require an AI assistant to engage in substantive, multi-turn dialogues. This could include virtual agents, chatbots, or question-answering systems that need to provide detailed, thoughtful responses. The model's ability to follow instructions and reason through problems makes it a good fit for educational or research applications as well. Things to try One interesting aspect of the SynthIA-7B-v1.3 model is its use of a "Tree of Thoughts" and "Chain of Thought" reasoning approach. You could experiment with prompts that ask the model to explicitly outline its step-by-step reasoning, exploring how it builds a logical flow of ideas to arrive at the final response. Additionally, you could test the model's ability to handle open-ended, multi-part instructions or prompts that require it to demonstrate flexible, contextual understanding.

Read more

Updated 5/21/2024

🧠

HelixNet

migtissera

Total Score

96

HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs - 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 provides a critique based on the provided answer to help modify/regenerate the answer. Finally, the regenerator takes in the critique and regenerates the answer. This actor-critic architecture is inspired by Reinforcement Learning algorithms, with the name derived from the spiral structure of a DNA molecule, symbolizing the intertwined nature of the three networks. Model inputs and outputs Inputs System-context**: The context for the task or question Question**: The question or prompt to be answered Outputs Response**: The initial response generated by the actor LLM Critique**: The feedback provided by the critic LLM on the initial response Regenerated response**: The final answer generated by the regenerator LLM based on the critique Capabilities HelixNet regenerates very pleasing and accurate responses, due to the entropy preservation of the regenerator. The actor network was trained on a large, high-quality dataset, while the critic network was trained on a smaller but carefully curated dataset. What can I use it for? HelixNet can be used for a variety of language generation tasks that benefit from an iterative refinement process, such as generating high-quality and coherent text responses. The architecture could be particularly useful for applications like conversational AI, question-answering, and content generation, where the model can leverage the feedback from the critic to improve the quality of the output. Things to try One interesting aspect of HelixNet is the incorporation of the critic network, which provides intelligent feedback to refine the initial response. You could experiment with prompting the model with different types of questions or system contexts and observe how the critic and regenerator work together to improve the overall quality of the output.

Read more

Updated 5/21/2024