HelixNet
Maintainer: migtissera
97
🧠
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
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
actorLLM - Critique: The feedback provided by the
criticLLM on the initial response - Regenerated response: The final answer generated by the
regeneratorLLM 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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🏷️
SynthIA-7B-v1.3
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.
Updated Invalid Date
🗣️
mixtralnt-4x7b-test
56
The mixtralnt-4x7b-test model is an experimental AI model created by the maintainer chargoddard. It is a Sparse Mixture of Experts (MoE) model that combines parts from several pre-trained Mistral models, including Q-bert/MetaMath-Cybertron-Starling, NeverSleep/Noromaid-7b-v0.1.1, teknium/Mistral-Trismegistus-7B, meta-math/MetaMath-Mistral-7B, and PocketDoc/Dans-AdventurousWinds-Mk2-7b. The maintainer is experimenting with a hack to populate the MoE gates in order to take advantage of the experts. Model inputs and outputs The mixtralnt-4x7b-test model is a text-to-text model, meaning it takes text as input and generates text as output. The specific input and output formats are not clearly defined, but the maintainer suggests the model may use an "alpaca??? or chatml??? format". Inputs Text prompts in an unspecified format, potentially related to alpaca or chatml Outputs Generated text in response to the input prompts Capabilities The mixtralnt-4x7b-test model is capable of generating coherent text, taking advantage of the experts from the combined Mistral models. However, the maintainer is still experimenting with the hack used to populate the MoE gates, so the full capabilities of the model are not yet known. What can I use it for? The mixtralnt-4x7b-test model could potentially be used for a variety of text generation tasks, such as creative writing, conversational responses, or other applications that require generating coherent text. However, since the model is still in an experimental stage, it's unclear how it would perform compared to more established language models. Things to try One interesting aspect of the mixtralnt-4x7b-test model is the maintainer's approach of combining parts of several pre-trained Mistral models into a Sparse Mixture of Experts. This technique could lead to improvements in the model's performance and capabilities, but the results are still unknown. It would be worth exploring the model's output quality, coherence, and consistency to see how it compares to other language models.
Updated Invalid Date
✅
MistralTrix-v1
110
MistralTrix-v1 is a further fine-tuned version of the zyh3826/GML-Mistral-merged-v1 model. Inspired by the RLHF process described by the authors of Intel/neural-chat-7b-v3-1, it has been optimized using Intel's dataset for neural-chat-7b-v3-1 and surpasses the original model on several benchmarks. The fine-tuning process took around an hour on a Google Colab A-1000 GPU with 40GB VRAM. Similar models include Mixtral-8x7B-v0.1 and NeuralHermes-2.5-Mistral-7B, which have also been fine-tuned using various techniques to improve performance. Model inputs and outputs Inputs Text Prompts**: The model takes in natural language text prompts as input. Outputs Generated Text**: The model outputs generated text that continues or completes the input prompt. Capabilities The MistralTrix-v1 model is a powerful text-to-text model capable of a wide variety of language tasks. It has demonstrated strong performance on several benchmarks, including the ARC, HellaSwag, MMLU, TruthfulQA, and Winogrande datasets. What can I use it for? With its broad capabilities, MistralTrix-v1 can be used for a variety of applications, such as: Content Generation**: Generating coherent and contextually relevant text for tasks like creative writing, story generation, and dialogue creation. Question Answering**: Answering questions on a diverse range of topics by leveraging the model's strong performance on the MMLU and TruthfulQA benchmarks. Task Completion**: Assisting with open-ended tasks that require language understanding and generation, such as summarization, translation, and code generation. Things to try One interesting aspect of MistralTrix-v1 is its ability to generate text that is both informative and engaging. Experiment with prompts that combine factual information with creative storytelling to see how the model can blend these elements. Another intriguing area to explore is the model's performance on specialized tasks or datasets that are more aligned with your specific use case. By understanding the model's strengths and limitations, you can better leverage its capabilities for your particular needs.
Updated Invalid Date
🤷
SynthIA-70B-v1.5
42
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 Invalid Date