Text Generation
Transformers
Safetensors
Chinese
English
PanguEmbedded
feature-extraction
causal-lm
conversational
custom_code
Eval Results (legacy)
Instructions to use killer66678/openpangu_7b_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use killer66678/openpangu_7b_lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="killer66678/openpangu_7b_lora", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("killer66678/openpangu_7b_lora", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use killer66678/openpangu_7b_lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "killer66678/openpangu_7b_lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "killer66678/openpangu_7b_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/killer66678/openpangu_7b_lora
- SGLang
How to use killer66678/openpangu_7b_lora 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 "killer66678/openpangu_7b_lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "killer66678/openpangu_7b_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "killer66678/openpangu_7b_lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "killer66678/openpangu_7b_lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use killer66678/openpangu_7b_lora with Docker Model Runner:
docker model run hf.co/killer66678/openpangu_7b_lora
- Xet hash:
- 2aa4b21a7013d0995b806a19d3b7ffffebc3c95abb36532ee0663919e2f1ebd9
- Size of remote file:
- 1.99 GB
- SHA256:
- 098f12201dc3b194cb3398806eb2bc4c4eeaadf06ded0946e466816be3f98e9e
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