Sensei-7B-V1

Maintainer: SciPhi

Total Score

84

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The Sensei-7B-V1 is a Large Language Model (LLM) fine-tuned from the mistral-ft-optimized-1218 model, which is based on the Mistral-7B model. Sensei-7B-V1 was fine-tuned with a fully synthetic dataset to specialize in performing retrieval-augmented generation (RAG) over detailed web search results. This model aims to generate accurate and well-cited summaries from a range of search results, providing more precise answers to user queries.

Similar models include the Mistral-7B-Instruct-v0.1, merlinite-7b, Mistral-7B-Instruct-v0.2, and Mixtral-8x7B-Instruct-v0.1. These models share similarities in their base architecture and fine-tuning approaches, though they may differ in specific capabilities and performance characteristics.

Model inputs and outputs

Inputs

  • Single search query: The model is designed to take a single search query as input and use it to generate a response.

Outputs

  • Retrieval-augmented generation: The model returns an answer that is generated using the context of the search results as background information.
  • JSON format: The model's output is structured in a JSON format that includes a summary of the search results and a list of related queries.

Capabilities

The Sensei-7B-V1 model specializes in using search to generate accurate and well-cited summaries. It can leverage detailed web search results to provide more precise answers to user queries, drawing upon the contextual information to produce informative responses.

What can I use it for?

The Sensei-7B-V1 model can be useful for applications that require generating detailed, fact-based responses to user questions or information requests. This could include chatbots, virtual assistants, or knowledge-based systems that need to provide accurate and well-supported information to users.

Things to try

One interesting aspect of the Sensei-7B-V1 model is its ability to utilize search results as context for generating responses. You could experiment with providing the model with different types of search queries, from factual questions to more open-ended information requests, and observe how it leverages the search context to formulate its answers.

Additionally, you could explore the model's performance on tasks that require synthesizing information from multiple sources, such as summarizing a set of web pages on a given topic or answering follow-up questions that build upon the initial search results.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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