Llama-3.1-Tulu-3-8B
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | Llama 3.1 Community License |
| Paper | arxiv:2411.15124 |
| Base Model | Llama-3.1-Tulu-3-8B-DPO |
What is Llama-3.1-Tulu-3-8B?
Llama-3.1-Tulu-3-8B is a state-of-the-art instruction-following model developed by Allen Institute for AI. It represents a significant advancement in open-source language models, specifically designed to excel at diverse tasks including mathematical reasoning, problem-solving, and general chat interactions. The model builds upon the Llama 3.1 architecture and implements advanced post-training techniques.
Implementation Details
The model utilizes a sophisticated training approach combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Value Rewards (RLVR). It employs the BF16 tensor type and includes comprehensive safety measures while maintaining high performance across various benchmarks.
- Implements custom chat template with user and assistant markers
- Supports efficient serving through VLLM
- Includes built-in tokenizer with chat template support
- Optimized for context length up to 8192 tokens
Core Capabilities
- Exceptional performance on MATH tasks (43.7% accuracy)
- Strong GSM8K problem-solving (87.6% accuracy)
- High safety scores across 6-task average (85.5%)
- Robust performance on MMLU (68.2% accuracy)
- Advanced code generation capabilities (83.9% HumanEval pass@10)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its balanced performance across diverse tasks, particularly excelling in mathematical reasoning and safety aspects while maintaining strong general capabilities. It's fully open-source with transparent training methodology and comprehensive documentation.
Q: What are the recommended use cases?
The model is particularly well-suited for mathematical problem-solving, coding tasks, and general instruction-following applications. It's designed for research and educational use, with strong safety considerations built-in.
