AMchat
A
Amchat
Overview :
AMchat is a large language model that integrates mathematical knowledge, higher-level mathematics learning questions, and their solutions. Based on the InternLM2-Math-7B model and fine-tuned with xtuner, it is specifically designed to answer higher-level mathematics problems. The project received Top12 ranking and the Innovation Creativity Award in the 2024 Puyu Large Model Series Competition (Spring Session), demonstrating its professional capabilities and innovativeness in the field of higher-level mathematics.
Target Users :
AMchat is primarily targeted towards learners and educators of higher-level mathematics, including college students, teachers, and researchers. It provides accurate solutions to mathematical problems, helping users deepen their understanding of higher-level mathematical concepts and problem-solving methods, thereby enhancing their learning efficiency.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 50.8K
Use Cases
College students use AMchat to solve higher-level mathematics homework problems
Teachers utilize AMchat to assist in teaching by providing standard problem-solving processes
Researchers employ AMchat for exploring and verifying mathematical problems
Features
Integrates higher-level mathematics knowledge and exercise question solutions
Built upon the InternLM2-Math-7B model
Fine-tuned and optimized using xtuner
Supports Docker deployment and local deployment
Provides detailed usage instructions and quick start guides
Supports model retraining and fine-tuning
Offers quantization and evaluation functionalities
How to Use
1. Clone the project to your local machine: git clone https://github.com/AXYZdong/AMchat.git
2. Create and activate a virtual environment: conda env create -f environment.yml && conda activate AMchat
3. Install necessary dependencies: pip install xtuner
4. Prepare the configuration file and download the model: Refer to the configuration file and download script in the repository
5. Fine-tune the model: xtuner train configuration file path
6. Convert the model format and deploy: Use the xtuner convert command to convert the model and deploy it as needed
7. Run the Demo or deploy the application on OpenXLab
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