Instructions to use IAAR-Shanghai/xVerify-1.5B-I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IAAR-Shanghai/xVerify-1.5B-I with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IAAR-Shanghai/xVerify-1.5B-I")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IAAR-Shanghai/xVerify-1.5B-I", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IAAR-Shanghai/xVerify-1.5B-I with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IAAR-Shanghai/xVerify-1.5B-I" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IAAR-Shanghai/xVerify-1.5B-I", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IAAR-Shanghai/xVerify-1.5B-I
- SGLang
How to use IAAR-Shanghai/xVerify-1.5B-I 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 "IAAR-Shanghai/xVerify-1.5B-I" \ --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": "IAAR-Shanghai/xVerify-1.5B-I", "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 "IAAR-Shanghai/xVerify-1.5B-I" \ --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": "IAAR-Shanghai/xVerify-1.5B-I", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IAAR-Shanghai/xVerify-1.5B-I with Docker Model Runner:
docker model run hf.co/IAAR-Shanghai/xVerify-1.5B-I
Add library_name, pipeline_tag and link to research paper
Hi! I'm Niels, part of the community science team at Hugging Face.
I'm opening this PR to improve the model card for xVerify-1.5B-I:
- Add
library_name: transformersto the metadata to enable the "Use in Transformers" button. - Add
pipeline_tag: text-generationto ensure the model is correctly categorized. - Link the model to its research paper: xVerify: Efficient Answer Verifier for Reasoning Model Evaluations.
- Credit the authors and link their Hugging Face profiles.
Feel free to merge if this looks good!
Hi Niels, thanks a lot for the PR and for the detailed improvements! π
These additions make the model card much clearer and more discoverable on the Hub. The metadata updates, paper reference, and GitHub link are all very helpful for users.
I'm happy to accept these changes and will merge the PR shortly. Thanks again for your contribution and for the support from the Hugging Face community team!