Cerebrum-1.0-8x7b

Maintainer: AetherResearch

Total Score

78

Last updated 5/17/2024

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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 Cerebrum-1.0-8x7b is a large language model (LLM) created by AetherResearch that is specifically designed for reasoning tasks. It is based on the Mixtral 8x7b model and has been fine-tuned on a small custom dataset of native chain of thought data. The model has also been further improved using a novel technique called targeted RLHF (tRLHF), which helps align the model with specific goals.

Unlike many other fine-tuning approaches, the training pipeline for Cerebrum-1.0-8x7b used fewer than 5,000 training prompts and even fewer labeled datapoints for tRLHF. This "native chain of thought" approach means the model is trained to devise a tactical plan before tackling problems that require thinking. As a result, for tasks like brainstorming, knowledge-intensive work, and creative endeavors, Cerebrum-1.0-8x7b will typically avoid unnecessarily verbose considerations.

When compared to other models like Gemini 1.0 Pro and GPT-3.5 Turbo, Cerebrum-1.0-8x7b offers competitive performance on a range of reasoning-focused tasks.

Model inputs and outputs

Inputs

  • The model accepts natural language prompts that describe a task or problem to be solved.

Outputs

  • The model generates natural language responses that provide a solution or approach to the given task or problem.

Capabilities

Cerebrum-1.0-8x7b excels at reasoning-focused tasks that require tactical planning and concise responses. For example, it performs well on benchmarks like ARC-C, HumanEval, GSM8k, and MATH, outperforming models like Gemini 1.0 Pro and GPT-3.5 Turbo.

What can I use it for?

The Cerebrum-1.0-8x7b model can be a valuable tool for a variety of applications that require reasoning and problem-solving capabilities. Some potential use cases include:

  • Business and strategic planning: The model's ability to generate concise, tactical responses could be helpful for brainstorming, analyzing complex scenarios, and developing business strategies.
  • Creative and knowledge-intensive tasks: Cerebrum-1.0-8x7b may be useful for tasks like ideation, research, and content creation, where it can provide focused, well-reasoned outputs.
  • Educational and academic applications: The model's strong performance on benchmarks like HumanEval and MATH suggests it could be a useful tool for educational purposes, such as tutoring, homework assistance, or test preparation.

Things to try

One interesting aspect of Cerebrum-1.0-8x7b is its "native chain of thought" approach, which trains the model to devise a tactical plan before tackling complex problems. To make the most of this capability, you could try prompting the model with open-ended, knowledge-intensive questions and observe how it approaches the task, breaking down the problem and providing a step-by-step solution.

Another interesting area to explore would be fine-tuning the model on specialized datasets or tasks that align with your specific use case. The model's strong performance on benchmarks suggests it may be a powerful foundation for further customization and adaptation.



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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