openbmb/Eurus-7b-sft
Eurus-7B-SFT is a 7 billion parameter language model developed by OpenBMB, fine-tuned from Mistral-7B. It is optimized for reasoning tasks, particularly in coding and mathematics, achieving strong performance against larger open-source models. The model leverages the UltraInteract dataset for fine-tuning, enhancing its capabilities in complex problem-solving and multi-turn interactions. Its primary strength lies in outperforming models five times its size in various reasoning domains.
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Eurus-7B-SFT: A Reasoning-Optimized LLM
Eurus-7B-SFT, developed by OpenBMB, is a 7 billion parameter language model fine-tuned from Mistral-7B. It is specifically designed and optimized for advanced reasoning tasks, demonstrating superior performance in domains like coding and mathematics.
Key Capabilities & Performance
- Optimized for Reasoning: Fine-tuned on the UltraInteract dataset, along with UltraChat, ShareGPT, and OpenOrca examples, to enhance reasoning abilities.
- Strong Performance: Achieves better overall performance than other open-source models of similar sizes and, in many cases, outperforms specialized models in their respective domains. Notably, Eurus-7B has shown to surpass baselines that are five times larger.
- Enhanced Math and Multi-turn Ability: Preference learning with UltraInteract further improves its capabilities in mathematical problem-solving and handling multi-turn conversations.
- Tailored Prompting: Utilizes specific prompt formats for coding and various math problem-solving approaches (Math-CoT, Math-PoT) to maximize performance.
When to Use This Model
- Complex Reasoning Tasks: Ideal for applications requiring robust logical deduction and problem-solving.
- Coding Assistance: Effective for generating Python code based on instructions.
- Mathematical Problem Solving: Suitable for step-by-step mathematical reasoning, including those requiring tool use like a Python interpreter.
- Resource-Constrained Environments: Offers competitive performance with a smaller parameter count, making it efficient compared to much larger models.