Vezora
Models by this creator
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Mistral-22B-v0.1
150
Mistral-22B-v0.1 is an experimental large language model developed by Vezora, a creator on the Hugging Face platform. This model is a culmination of knowledge distilled from various experts into a single, dense 22B parameter model. It is not a singular trained expert, but rather a compressed mixture-of-experts (MoE) model converted into a dense 22B architecture. The model is related to other Mistral models such as the Mixtral-8x22B-v0.1 and Mixtral-8x7B-v0.1, which are also sparse MoE models from the Mistral AI team. However, Mistral-22B-v0.1 represents the first working MoE to dense model conversion effort. Model inputs and outputs Mistral-22B-v0.1 is a large language model capable of processing and generating human-like text. The model takes in text-based prompts as input and produces relevant, coherent text as output. Inputs Text-based prompts, questions, or instructions provided to the model Outputs Relevant, human-like text generated in response to the input The model can be used for a variety of text-based tasks such as question answering, language generation, and more Capabilities The Mistral-22B-v0.1 model exhibits strong mathematical abilities, despite not being explicitly trained on math-focused data. This suggests the model has learned robust reasoning capabilities that can be applied to a range of tasks. What can I use it for? The Mistral-22B-v0.1 model can be used for a variety of natural language processing tasks, such as: Question answering: The model can be prompted with questions and provide relevant, informative answers. Language generation: The model can generate human-like text on a given topic or in response to a prompt. Summarization: The model can condense and summarize longer pieces of text. Brainstorming and ideation: The model can generate creative ideas and solutions to open-ended prompts. Things to try One interesting aspect of Mistral-22B-v0.1 is its experimental nature. As an early prototype, the model has been trained on a relatively small dataset compared to the upcoming version 2 release. This means the model's performance may not be as polished as more mature language models, but it presents an opportunity to explore the model's capabilities and provide feedback to the Vezora team. Prompts that test the model's reasoning skills, such as math-related questions or open-ended problem-solving tasks, could be particularly insightful. Additionally, testing the model's ability to handle multi-turn conversations or code generation tasks could yield valuable insights as the Mistral team continues to develop the model.
Updated 5/28/2024
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Mistral-22B-v0.2
108
Mistral-22B-v0.2 is an experimental 22B parameter generative language model developed by Vezora. It builds upon the earlier Mistral-22B-v0.1 model, incorporating several key enhancements. This model is not a single expert, but rather a compressed Mixture of Experts (MOE) model that has been converted into a dense 22B parameter model. Compared to the previous version, Mistral-22B-v0.2 has been trained on 8x more data, resulting in significant improvements across various capabilities. The Mistral-22B-v0.1 model, also developed by Vezora, was an earlier experimental 22B parameter model that exhibited strong mathematical abilities and coding proficiency, despite not being explicitly trained on those tasks. Model Inputs and Outputs Mistral-22B-v0.2 is a text-to-text generative model, capable of producing coherent and contextual responses based on the provided input prompts. Inputs Freeform text prompts that can cover a wide range of topics, from general conversation to task-oriented instructions. The model uses the GUANACO prompt format, which has been optimized for best results. Outputs The model generates relevant and contextual text responses, up to 32,000 tokens in length. It can handle multi-turn conversations, providing coherent and consistent responses across multiple exchanges. The model has also been trained to output responses in JSON format, allowing for structured data generation. Capabilities Mistral-22B-v0.2 exhibits several key capabilities that set it apart from the previous version: Improved Mathematical Proficiency**: The model demonstrates enhanced mathematical abilities, despite not being explicitly trained on mathematical tasks. Enhanced Coding Skills**: The model can now successfully complete simple coding tasks, such as generating HTML with a color-changing button, which the v0.1 model struggled with. More Coherent Responses**: The v0.2 model provides more cohesive and context-appropriate responses, better understanding the prompts and providing relevant answers. Highly Uncensored**: This model has been realigned to be uncensored, allowing it to respond to a wide range of prompts without restrictions. Multitask Capabilities**: The model has been trained on diverse datasets, including multi-turn conversations and agent-based tasks, expanding its versatility. JSON Support**: The model can now generate responses in JSON format, enabling structured data output. What can I use it for? Mistral-22B-v0.2 can be a powerful tool for a variety of applications, including: Conversational AI**: The model's ability to engage in multi-turn dialogues and provide coherent responses makes it suitable for chatbot and virtual assistant development. Content Generation**: The model can be used to generate diverse content, such as articles, stories, or even code snippets, across a wide range of topics. Task Assistance**: The model's capabilities in areas like coding and JSON generation can be leveraged to assist with technical tasks and data manipulation. Research and Exploration**: As an experimental model, Mistral-22B-v0.2 can be a valuable resource for researchers and developers interested in pushing the boundaries of large language models. Things to try When using Mistral-22B-v0.2, consider exploring its uncensored capabilities, but be mindful of the potential risks. Additionally, try prompting the model with coding-related tasks or requests for structured data in JSON format to better understand its expanded capabilities. Remember to always use the GUANACO prompt format for optimal results, as specified by the model's maintainer. Engaging in multi-turn conversations can also help you better assess the model's coherence and contextual understanding.
Updated 5/28/2024