Instructions to use Jiabin99/GraphGPT-7B-mix-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jiabin99/GraphGPT-7B-mix-all with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jiabin99/GraphGPT-7B-mix-all")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Jiabin99/GraphGPT-7B-mix-all", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Jiabin99/GraphGPT-7B-mix-all with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jiabin99/GraphGPT-7B-mix-all" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jiabin99/GraphGPT-7B-mix-all", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jiabin99/GraphGPT-7B-mix-all
- SGLang
How to use Jiabin99/GraphGPT-7B-mix-all 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 "Jiabin99/GraphGPT-7B-mix-all" \ --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": "Jiabin99/GraphGPT-7B-mix-all", "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 "Jiabin99/GraphGPT-7B-mix-all" \ --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": "Jiabin99/GraphGPT-7B-mix-all", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jiabin99/GraphGPT-7B-mix-all with Docker Model Runner:
docker model run hf.co/Jiabin99/GraphGPT-7B-mix-all
GraphGPT
GraphGPT is a graph-oriented Large Language Model tuned by Graph Instruction Tuning paradigm.
Model Details
GraphGPT is a graph-oriented Large Language Model tuned by Graph Instruction Tuning paradigm based on the Vicuna-7B-v1.5 model.
- Developed by: Data Intelligence Lab@HKU
- Model type: An auto-regressive language model based on the transformer architecture.
- Finetuned from model: Vicuna-7B-v1.5 model.
Model Sources
- Repository: https://github.com/HKUDS/GraphGPT
- Paper:
- Project: https://graphgpt.github.io/
Uses
This version of GraphGPT is tuned utilizing the mixing instruction data, which is able to handle both node classification and link prediction for different graph datasets.
How to Get Started with the Model
- Command line interface: Plaese refer to https://github.com/HKUDS/GraphGPT to evaluate our GraphGPT.
- Gradio demo is under development.
- Downloads last month
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docker model run hf.co/Jiabin99/GraphGPT-7B-mix-all