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Eurus 2 7B SFT
Overview :
Eurus-2-7B-SFT is a large language model fine-tuned from the Qwen2.5-Math-7B model, aimed at enhancing mathematical reasoning and problem-solving abilities. The model learns reasoning patterns through imitation learning (supervised fine-tuning), effectively solving complex mathematical and programming tasks. Its main advantages lie in its powerful reasoning capabilities and accurate handling of mathematical problems, making it suitable for scenarios that require complex logical reasoning. Developed by the PRIME-RL team, the model aims to improve its reasoning capabilities through implicit rewards.
Target Users :
This product is suitable for professionals, researchers, and students who need to tackle complex mathematical problems and programming tasks. It helps users quickly generate solutions while providing detailed reasoning processes to enhance understanding and verification.
Use Cases
Solve complex mathematical problems, such as comparing two decimals
Generate Python code to address programming challenges
Perform multi-step reasoning tasks to resolve problems incrementally
Features
Supports reasoning and solving of mathematical problems, capable of outputting answers in LaTeX format
Provides code generation capabilities for programming tasks, supporting Python language
Employs imitation learning methods, demonstrating strong learning ability for reasoning patterns
Supports various reasoning actions, such as evaluation, progression, and validation, to solve problems step by step
Facilitates step-by-step reasoning and solution generation for complex problems
Offers detailed records of the reasoning process for easier understanding and verification
Supports training and optimization on large-scale datasets to enhance the model's reasoning capabilities
How to Use
1. Prepare your question: Organize the mathematical problem or programming task into text format.
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2. Use system prompts: Select the appropriate system prompt based on the problem type, such as a math problem prompt or a programming problem prompt.
3. Input your question: Enter both the question and system prompt into the model.
4. Obtain results: The model will generate a detailed reasoning process and solution.
5. Validate answers: Check the accuracy of the model's generated answers and make adjustments as needed.
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