Senku-70B-Full

Maintainer: ShinojiResearch

Total Score

139

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

Senku-70B-Full is a large language model developed by ShinojiResearch, a team of AI researchers and engineers. This model is a fine-tuned version of the 152334H/miqu-1-70b-sf model, which was originally trained on a synthesized Wikipedia conversation dataset. The fine-tuning process utilized the Slimorca dataset and a custom LoRA adapter to achieve state-of-the-art performance on several benchmark tasks.

Compared to similar models like neural-chat-7b-v3-3 and 7B, Senku-70B-Full boasts impressive capabilities across a range of domains, including text generation, question answering, and commonsense reasoning.

Model inputs and outputs

Inputs

  • Raw text prompts that can be used to guide the model's generation, such as instructions, queries, or dialogue contexts.

Outputs

  • Fluent, coherent text continuations that align with the provided input prompt.
  • Responses to questions or information requests.
  • Logical inferences and explanations based on the input context.

Capabilities

The Senku-70B-Full model has demonstrated strong performance on a variety of benchmark tasks, including the EQ-Bench, GSM8k, and Hellaswag. It can engage in thoughtful, contextually-appropriate dialogue, offer insightful analysis and commentary, and tackle complex reasoning problems. The model's broad knowledge and language understanding make it suitable for use in a wide range of applications, from chatbots and virtual assistants to content generation and question-answering systems.

What can I use it for?

With its impressive capabilities, the Senku-70B-Full model can be leveraged for a variety of applications, such as:

  • Building conversational AI assistants that can engage in natural, informative dialogue
  • Generating high-quality written content, such as articles, stories, or scripts
  • Powering question-answering systems that can provide accurate and detailed responses
  • Enhancing search and recommendation engines with advanced language understanding
  • Enabling more sophisticated and personalized interactions in customer service and support applications

Things to try

One interesting aspect of the Senku-70B-Full model is its ability to adapt to different prompt formats, such as the ChatML template used in the neural-chat-7b-v3-3 model. Experimenting with various prompt styles and structures can help you unlock the model's full potential and find the most effective way to leverage its capabilities for your specific use case.

Additionally, you may want to explore the model's performance on different types of tasks, such as creative writing, code generation, or multi-turn dialogue, to better understand its strengths and limitations. Comparing its outputs and behavior to other large language models can also provide valuable insights.



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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