Instructions to use li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint") model = AutoModelForCausalLM.from_pretrained("li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint
- SGLang
How to use li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint 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 "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint" \ --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": "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint", "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 "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint" \ --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": "li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint with Docker Model Runner:
docker model run hf.co/li-jay-cs/test-4gpu-gpt2-gcp-supervised-summarize-checkpoint
- Xet hash:
- 4a7f00eb4e50e62d4caf268889aae42e760a18372b271f740418ac615080e3bd
- Size of remote file:
- 6.2 kB
- SHA256:
- fcb79ddc73a39fdf21bb850032893e85e4bffb6825ab5422f1d297652633e297
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