Llama-3.1-Tulu-3-70B-DPO
| Property | Value |
|---|---|
| License | Llama 3.1 Community License |
| Base Model | allenai/Llama-3.1-Tulu-3-70B-SFT |
| Primary Language | English |
| Training Repository | https://github.com/allenai/open-instruct |
What is Llama-3.1-Tulu-3-70B-DPO?
Llama-3.1-Tulu-3-70B-DPO is a sophisticated language model that represents a significant advancement in the Tülu3 model family. Built upon the Llama 3.1 architecture and optimized through Direct Preference Optimization (DPO), this model excels at instruction following and demonstrates exceptional performance across various tasks including mathematical reasoning, coding, and general knowledge.
Implementation Details
The model was trained using specific hyperparameters including a learning rate of 2.0e-7, a linear learning rate schedule, and an effective batch size of 128. It supports a maximum sequence length of 2,048 tokens and underwent one epoch of training. The model utilizes a specialized chat template for structured interactions.
- Advanced DPO training methodology for enhanced performance
- Comprehensive evaluation across multiple benchmarks
- Integrated safety considerations and ethical guidelines
- Optimized for both general conversation and specialized tasks
Core Capabilities
- Strong performance in mathematical reasoning (MATH, GSM8K)
- Exceptional results in code generation (HumanEval)
- High accuracy in multi-task benchmarks (MMLU, BigBenchHard)
- Robust safety features with 88.3% average safety score
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its comprehensive training approach combining SFT and DPO techniques, achieving state-of-the-art performance across diverse tasks while maintaining strong safety characteristics. It's particularly notable for its balanced performance across both specialized (like MATH and coding) and general-purpose tasks.
Q: What are the recommended use cases?
The model is well-suited for research and educational applications, particularly excelling in mathematical reasoning, code generation, and general instruction following. It's designed for both technical tasks and general conversation, making it versatile for various applications while adhering to responsible AI use guidelines.
