Instructions to use sjbiohack/biogpt-finetuned-KIDS2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjbiohack/biogpt-finetuned-KIDS2023 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sjbiohack/biogpt-finetuned-KIDS2023")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sjbiohack/biogpt-finetuned-KIDS2023") model = AutoModelForCausalLM.from_pretrained("sjbiohack/biogpt-finetuned-KIDS2023", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sjbiohack/biogpt-finetuned-KIDS2023 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sjbiohack/biogpt-finetuned-KIDS2023" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sjbiohack/biogpt-finetuned-KIDS2023", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sjbiohack/biogpt-finetuned-KIDS2023
- SGLang
How to use sjbiohack/biogpt-finetuned-KIDS2023 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 "sjbiohack/biogpt-finetuned-KIDS2023" \ --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": "sjbiohack/biogpt-finetuned-KIDS2023", "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 "sjbiohack/biogpt-finetuned-KIDS2023" \ --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": "sjbiohack/biogpt-finetuned-KIDS2023", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sjbiohack/biogpt-finetuned-KIDS2023 with Docker Model Runner:
docker model run hf.co/sjbiohack/biogpt-finetuned-KIDS2023
Download pytorch_model.bin from sjbiohack/biogpt-finetuned-KIDS2023: direct link, hf CLI and curl.
- Browser
- Download file 1.39 GB
-
https://huggingface.co/sjbiohack/biogpt-finetuned-KIDS2023/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://sjbiohack/biogpt-finetuned-KIDS2023@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sjbiohack/biogpt-finetuned-KIDS2023/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.39 GB
- Xet hash:
- 25f26fe4f67727eb759e494759a2c91ba7e5624af683745d88f891f53a0391fb
- Size of remote file:
- 1.39 GB
- SHA256:
- cdbf613c6795f80a9e11156582c7a79659fb642011d541d1d59ccf82a66b76ec
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