Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use dev-ninja/tsel_distilgpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dev-ninja/tsel_distilgpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dev-ninja/tsel_distilgpt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dev-ninja/tsel_distilgpt") model = AutoModelForCausalLM.from_pretrained("dev-ninja/tsel_distilgpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dev-ninja/tsel_distilgpt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dev-ninja/tsel_distilgpt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dev-ninja/tsel_distilgpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dev-ninja/tsel_distilgpt
- SGLang
How to use dev-ninja/tsel_distilgpt 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 "dev-ninja/tsel_distilgpt" \ --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": "dev-ninja/tsel_distilgpt", "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 "dev-ninja/tsel_distilgpt" \ --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": "dev-ninja/tsel_distilgpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dev-ninja/tsel_distilgpt with Docker Model Runner:
docker model run hf.co/dev-ninja/tsel_distilgpt
- Xet hash:
- c9fdff5406d5cf3738c9a04a0c60d6e36beddb5f32fad0ee37bdf9a92a7a0ae4
- Size of remote file:
- 328 MB
- SHA256:
- aaeca27fd8b8212ef81efe1f6a64194380332796ae47d84d0a9231ea75d20116
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