Llama-3.1-Tulu-3-8B
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | Llama 3.1 Community License |
| Paper | View Paper |
| Base Model | Llama-3.1-Tulu-3-8B-DPO |
| Training Type | RLVR (Reinforcement Learning with Value Rewards) |
What is Llama-3.1-Tulu-3-8B?
Llama-3.1-Tulu-3-8B is a state-of-the-art instruction-following language model developed by Allen AI. It represents the culmination of a sophisticated training pipeline that includes Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and RLVR training stages. The model is particularly notable for its strong performance across diverse tasks, including mathematical reasoning, general knowledge, and conversational abilities.
Implementation Details
The model leverages the Llama 3.1 architecture and implements a specialized chat template format using user and assistant tokens. It's optimized for deployment with VLLM and supports a context length of up to 8192 tokens. The training process involved careful hyperparameter tuning, including a learning rate of 3×10⁻⁷ and specific PPO settings for optimal performance.
- Specialized chat template with user/assistant format
- VLLM deployment support
- Optimized for both conversation and complex reasoning tasks
- Implements advanced RLVR training methodology
Core Capabilities
- Strong performance on MATH and GSM8K benchmarks (43.7% and 87.6% respectively)
- Robust instruction following with 82.4% on IFEval
- High safety scores across multiple benchmarks (85.5% average)
- Effective code generation capabilities (83.9% on HumanEval)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its comprehensive training approach combining SFT, DPO, and RLVR, resulting in balanced performance across both traditional chat tasks and complex reasoning challenges. It's particularly notable for achieving strong results despite its relatively compact 8B parameter size.
Q: What are the recommended use cases?
The model excels in mathematical reasoning, code generation, and instruction following tasks. It's particularly well-suited for applications requiring a balance of conversational ability and technical problem-solving, though users should note the standard AI safety and bias considerations.
