Instructions to use yhavinga/gpt-neo-125M-dutch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yhavinga/gpt-neo-125M-dutch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yhavinga/gpt-neo-125M-dutch")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("yhavinga/gpt-neo-125M-dutch") model = AutoModelForMultimodalLM.from_pretrained("yhavinga/gpt-neo-125M-dutch") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yhavinga/gpt-neo-125M-dutch with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yhavinga/gpt-neo-125M-dutch" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yhavinga/gpt-neo-125M-dutch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yhavinga/gpt-neo-125M-dutch
- SGLang
How to use yhavinga/gpt-neo-125M-dutch 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 "yhavinga/gpt-neo-125M-dutch" \ --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": "yhavinga/gpt-neo-125M-dutch", "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 "yhavinga/gpt-neo-125M-dutch" \ --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": "yhavinga/gpt-neo-125M-dutch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yhavinga/gpt-neo-125M-dutch with Docker Model Runner:
docker model run hf.co/yhavinga/gpt-neo-125M-dutch
- Xet hash:
- dc13790465126161fd3591f2839c783e7a01c5744f8a4a6ff519c8f8dc02a49a
- Size of remote file:
- 551 MB
- SHA256:
- c54825286d9009e78d2d8615936499f8e90706b1bb432e75807a6b94df65a73d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.