Migtissera
Models by this creator
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SynthIA-7B-v1.3
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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.
Updated 5/28/2024
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HelixNet
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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.
Updated 5/28/2024
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SynthIA-70B-v1.5
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The SynthIA-70B-v1.5 model is a large language model developed by the AI researcher migtissera. It is built upon the Mistral-7B-v0.1 base model and has been fine-tuned for instruction following and long-form conversations. The model is part of the SynthIA series, which includes other models like the SynthIA-7B-v1.3. These models are uncensored and intended to be used with caution. Model inputs and outputs The SynthIA-70B-v1.5 model is designed to accept natural language instructions and engage in open-ended conversations. It utilizes a specialized prompt format to evoke "Tree of Thought" and "Chain of Thought" reasoning, which encourages the model to explore multiple lines of reasoning and backtrack when necessary to construct a clear, cohesive response. Inputs Instruction prompts**: Natural language instructions or questions that the model should respond to, often following a specific format such as: SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: Outputs Detailed, multi-paragraph responses**: The model generates a coherent, well-reasoned response that addresses the input prompt, often incorporating relevant concepts, examples, and step-by-step explanations. Capabilities The SynthIA-70B-v1.5 model demonstrates strong capabilities in areas such as: Instruction following and task completion Open-ended conversation and dialogue Analytical and problem-solving abilities Knowledge synthesis and storytelling For example, the model can provide detailed explanations for complex scientific or technical topics, generate creative narratives, and engage in thoughtful discussions on a wide range of subjects. What can I use it for? The SynthIA-70B-v1.5 model could be useful for a variety of applications, such as: Educational and informational content generation Interactive virtual assistants and chatbots Creative writing and worldbuilding Specialized domain-specific applications (e.g., technical support, research assistance) However, it's important to note that the model is uncensored, so users should exercise caution and carefully consider the potential impacts of the model's outputs. Things to try One interesting aspect of the SynthIA-70B-v1.5 model is its ability to engage in multi-step reasoning and backtracking. You could try providing the model with complex, open-ended prompts that require it to explore multiple lines of thought and adjust its responses based on the provided context and feedback. This could lead to more insightful and nuanced outputs that showcase the model's analytical capabilities. Another area to explore is the model's handling of mathematical and scientific concepts. The provided examples demonstrate the model's ability to generate MathJSON solutions, which could be a useful feature for educational or research-oriented applications.
Updated 9/6/2024