Instructions to use RumuH/dia-pangu-bin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RumuH/dia-pangu-bin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RumuH/dia-pangu-bin")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RumuH/dia-pangu-bin", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RumuH/dia-pangu-bin with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RumuH/dia-pangu-bin" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RumuH/dia-pangu-bin", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RumuH/dia-pangu-bin
- SGLang
How to use RumuH/dia-pangu-bin with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "RumuH/dia-pangu-bin" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RumuH/dia-pangu-bin", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "RumuH/dia-pangu-bin" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RumuH/dia-pangu-bin", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RumuH/dia-pangu-bin with Docker Model Runner:
docker model run hf.co/RumuH/dia-pangu-bin
Dia-Pangu
Dia-Pangu is a domain-adapted large language model trained based on the OpenPangu-7B architecture.
This repository provides the trained model weights for both Chinese and English versions.
๐ฆ Model Weights
This repository contains two sets of model weights:
CN/pytorch_model.binโ Chinese versionEN/pytorch_model.binโ English version
These weights were trained as part of our Dia-Pangu project.
For full training details, architecture, and usage instructions, please refer to the official GitHub repository:
๐ https://github.com/chunyu-atx/Dia-Pangu
๐ Usage
The detailed inference and fine-tuning instructions are provided in the GitHub repository:
https://github.com/chunyu-atx/Dia-Pangu
Please follow the setup instructions there to properly load and run the model.
๐ Model Description
The core functions of Dia-Pangu are CT image recognition and generation of Chinese and English reports.\ This model was trained using the CTRG-Chest-548K dataset and underwent Lora fine-tuning.
โ ๏ธ Important Notes
- This repository only contains model weights.
- The full model implementation and loading scripts are available in the GitHub repository.
- Make sure to use the correct tokenizer and configuration files provided in the main project repository.