Instructions to use zlsl/m_physics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zlsl/m_physics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zlsl/m_physics")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zlsl/m_physics") model = AutoModelForCausalLM.from_pretrained("zlsl/m_physics") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zlsl/m_physics with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zlsl/m_physics" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zlsl/m_physics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zlsl/m_physics
- SGLang
How to use zlsl/m_physics 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 "zlsl/m_physics" \ --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": "zlsl/m_physics", "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 "zlsl/m_physics" \ --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": "zlsl/m_physics", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zlsl/m_physics with Docker Model Runner:
docker model run hf.co/zlsl/m_physics
- Xet hash:
- 1ade1da4bf54dc8d32defe5c7fedda33f629beabe6b6daeca3a0fa964b3c6cb1
- Size of remote file:
- 3.9 kB
- SHA256:
- 572f4d7f86b83c9ac7ae523c7e16e21c75c53a3e412953f68a5ea4ff8c4fd940
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