Cerebrum-1.0-8x7b
| Property | Value |
|---|---|
| Parameter Count | 46.7B |
| Base Model | Mixtral-8x7B-v0.1 |
| License | Apache 2.0 |
| Format | FP16 |
What is Cerebrum-1.0-8x7b?
Cerebrum-1.0-8x7b is an advanced language model specifically designed for complex reasoning tasks. Built upon the Mixtral-8x7B architecture, it has been fine-tuned using a unique approach that combines native chain-of-thought data and targeted RLHF (tRLHF). What sets it apart is its efficient training pipeline, utilizing fewer than 5,000 training prompts and a select number of labeled datapoints for tRLHF.
Implementation Details
The model employs a native chain-of-thought approach, training it to develop tactical plans before tackling complex problems. It operates efficiently at low temperatures and shows competitive performance against models like Gemini 1.0 Pro and GPT-3.5 Turbo.
- Architecture based on Mixtral-8x7B-v0.1
- Implements targeted RLHF for efficient alignment
- Optimized for zero-shot reasoning tasks
- Uses Alpaca-style templating for optimal performance
Core Capabilities
- Strong performance in mathematical reasoning and problem-solving
- Efficient handling of complex logical tasks
- Competitive benchmarking scores on ARC-C, HumanEval, GSM8k, and MATH datasets
- Natural chain-of-thought reasoning without unnecessary verbosity
- Self-consistent and precise responses at low temperatures
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its efficient training approach using fewer than 5,000 prompts and its native chain-of-thought capabilities, allowing it to tackle complex reasoning tasks with a strategic approach.
Q: What are the recommended use cases?
The model excels in tasks requiring detailed reasoning, mathematical problem-solving, and logical analysis. It's particularly well-suited for applications needing step-by-step problem decomposition and explicit thought processes.
