AutoMR for Pangu

This project equips the Pangu model with the AutoMR reasoning framework, optimized for Huawei Ascend hardware.

🌳 Project Structure

.
β”œβ”€β”€ AMC.SH
β”œβ”€β”€ automr
β”‚   β”œβ”€β”€ config.py
β”‚   β”œβ”€β”€ dag.py
β”‚   β”œβ”€β”€ data_loader.py
β”‚   β”œβ”€β”€ evaluator.py
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ model.py
β”‚   β”œβ”€β”€ strategies.py
β”‚   β”œβ”€β”€ trainer.py
β”‚   β””── utils.py
β”œβ”€β”€ checkpoints
β”‚   β””── MATH
β”œβ”€β”€ embedder_server.sh
β”œβ”€β”€ generator_server.sh
β”œβ”€β”€ main.py
β”œβ”€β”€ math_train.sh
└── processed_data
    β”œβ”€β”€ AMC
    └── MATH

πŸ”§ 1. Installation

a. Clone the Project Repository

hf download Alexhf825/AutoMR-pangu --local-dir AutoMR-pangu
cd AutoMR-pangu

b. Install Dependencies


c. Download Datasets

This command will download the datasets and place them in the ./processed_data directory, matching the project structure.

hf download Alexhf825/dataset-AutoMR-pangu --repo-type=dataset --local-dir=./

πŸš€ 2. Start the Servers

This project requires two services running in an OpenAI-API style. Please run the following commands in two separate terminal sessions.

Start the Embedder Server:

bash embedder_server.sh

Start the Generator Server:

bash generator_server.sh

πŸ“ˆ 3. Run Evaluation

A pre-trained checkpoint (MATH) is provided. You can directly evaluate the model on the AMC dataset using the following command:

bash AMC.sh

πŸ‹οΈ 4. Run Training

You can also train the model from scratch on the MATH dataset by running:

bash math_train.sh
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Model tree for Alexhf825/AutoMR-pangu

Finetuned
(1)
this model

Dataset used to train Alexhf825/AutoMR-pangu