Cerebrum-1.0-7b

Maintainer: AetherResearch

Total Score

50

Last updated 5/21/2024

🛸

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-1.0-7b is a large language model (LLM) created by AetherResearch specifically for reasoning tasks. It is based on the Mistral 7b model, fine-tuned on a small custom dataset of native chain of thought data and further improved with targeted RLHF (tRLHF), a novel technique for sample-efficient LLM alignment. Unlike numerous other recent fine-tuning approaches, the training pipeline for Cerebrum-1.0-7b includes under 5000 training prompts and even fewer labeled datapoints for tRLHF.

The native chain of thought approach means that Cerebrum is trained to devise a tactical plan before tackling problems that require thinking. For brainstorming, knowledge intensive, and creative tasks, Cerebrum will typically omit unnecessarily verbose considerations. On benchmarks that require reasoning, such as ARC Challenge, GSM8k, and Math, zero-shot prompted Cerebrum-1.0-7b significantly outperforms few-shot prompted Mistral 7b as well as much larger models like Llama 2 70b.

Another model in the Cerebrum family, Cerebrum-1.0-8x7b, is a larger version that offers competitive performance to Gemini 1.0 Pro and GPT-3.5 Turbo on a range of reasoning tasks.

Model inputs and outputs

Inputs

  • Text prompts: Cerebrum-1.0-7b is designed to perform best when prompted with an Alpaca-style template that requests the description of the "thought process". This encourages the model to think through the problem and provide a detailed explanation of its reasoning.

Outputs

  • Text responses: The model will generate a response that describes its thought process and provides a helpful, detailed answer to the user's question or prompt.

Capabilities

Cerebrum-1.0-7b excels at tasks that require reasoning, such as solving math problems, answering questions that involve complex logic, and generating creative and knowledge-intensive content. The model's performance on benchmarks like ARC Challenge, GSM8k, and Math demonstrates its ability to tackle these types of tasks effectively.

What can I use it for?

The Cerebrum-1.0-7b model could be useful for a variety of applications that require robust reasoning and problem-solving capabilities, such as:

  • Educational tools: The model could be used to create interactive learning experiences that guide students through complex problems and encourage them to think critically.
  • Research and analysis: Cerebrum-1.0-7b could be used to assist researchers in tasks like literature review, data analysis, and hypothesis generation.
  • Creative writing and ideation: The model's ability to generate thoughtful, detailed responses could make it a valuable tool for writers, marketers, and other creative professionals.

Things to try

One interesting aspect of Cerebrum-1.0-7b is its "native chain of thought" approach, which encourages the model to devise a tactical plan before attempting to solve a problem. This could be a useful technique for applications that require a structured, step-by-step problem-solving process, such as tutoring systems or task-oriented chatbots.

Additionally, the model's strong performance on reasoning-focused benchmarks suggests that it could be a valuable starting point for further fine-tuning or customization, particularly for applications that require advanced logical reasoning or complex problem-solving skills.



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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