Text Generation
Transformers
PyTorch
zhinao
mteb
qihoo360
奇虎360
RAG-reranking
custom_code
Eval Results (legacy)
Instructions to use qihoo360/360Zhinao-1.8B-Reranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qihoo360/360Zhinao-1.8B-Reranking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="qihoo360/360Zhinao-1.8B-Reranking", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("qihoo360/360Zhinao-1.8B-Reranking", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use qihoo360/360Zhinao-1.8B-Reranking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qihoo360/360Zhinao-1.8B-Reranking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qihoo360/360Zhinao-1.8B-Reranking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qihoo360/360Zhinao-1.8B-Reranking
- SGLang
How to use qihoo360/360Zhinao-1.8B-Reranking 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 "qihoo360/360Zhinao-1.8B-Reranking" \ --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": "qihoo360/360Zhinao-1.8B-Reranking", "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 "qihoo360/360Zhinao-1.8B-Reranking" \ --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": "qihoo360/360Zhinao-1.8B-Reranking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qihoo360/360Zhinao-1.8B-Reranking with Docker Model Runner:
docker model run hf.co/qihoo360/360Zhinao-1.8B-Reranking
- Xet hash:
- 204f6284176c3738b133fd23035ce8373d213d347c00ffa75641c4b1a0347c58
- Size of remote file:
- 3.76 GB
- SHA256:
- 633b9fe5d1024d306777843f465188b1875561ed96ff3cc9253e12c312c1f38e
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