OLMo-2-1124-13B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 13.7B |
| License | Apache 2.0 |
| Paper | Link |
| Base Model | OLMo-2-13B-1124-DPO |
What is OLMo-2-1124-13B-Instruct?
OLMo-2-1124-13B-Instruct is an advanced language model developed by Allen Institute for AI as part of their OLMo (Open Language Model) series. This model represents a significant advancement in open-source AI, featuring 13.7B parameters and specialized training for instruction-following capabilities. It's built upon the base OLMo-2 13B model and has undergone multiple training phases including supervised fine-tuning on the Tülu 3 dataset, DPO training, and RLVR training.
Implementation Details
The model implements a sophisticated architecture trained specifically for instruction-following tasks. It uses BF16 tensor type and includes a specific chat template format for consistent interaction. The training process involved multiple stages, culminating in RLVR training with carefully tuned hyperparameters including a 4×10⁻⁷ learning rate and 2,048 token length capacity.
- Specialized training on math, reasoning, and general instruction-following tasks
- Implements advanced PPO settings for RLVR training
- Supports both chat and task-specific applications
- Includes built-in chat templating system
Core Capabilities
- Strong performance on mathematical reasoning (87.4% on GSM8k)
- Advanced instruction following and chat capabilities
- High safety scores (77.5% on safety benchmarks)
- Robust performance across multiple evaluation metrics including MMLU (68.6%)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its fully open nature and strong performance across diverse tasks, particularly in mathematics and reasoning. It's also notable for its transparent training process and comprehensive documentation.
Q: What are the recommended use cases?
The model excels in mathematical problem-solving, general instruction following, and chat applications. It's particularly well-suited for research and educational purposes, with specific strength in tasks requiring logical reasoning and mathematical computation.
