Text Generation
Transformers
PyTorch
English
gpt2
scaling-study
benchmarking
banterhearts
text-generation-inference
Instructions to use Crusadersk/tiny-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crusadersk/tiny-gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Crusadersk/tiny-gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Crusadersk/tiny-gpt2") model = AutoModelForCausalLM.from_pretrained("Crusadersk/tiny-gpt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Crusadersk/tiny-gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Crusadersk/tiny-gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Crusadersk/tiny-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Crusadersk/tiny-gpt2
- SGLang
How to use Crusadersk/tiny-gpt2 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 "Crusadersk/tiny-gpt2" \ --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": "Crusadersk/tiny-gpt2", "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 "Crusadersk/tiny-gpt2" \ --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": "Crusadersk/tiny-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Crusadersk/tiny-gpt2 with Docker Model Runner:
docker model run hf.co/Crusadersk/tiny-gpt2
Download model.safetensors from Crusadersk/tiny-gpt2: direct link, hf CLI and curl.
- Browser
- Download file 2.51 MB
-
https://huggingface.co/Crusadersk/tiny-gpt2/resolve/67c8e486df0cca7c0490c621eeb5c6e624a36df7/model.safetensors
- Command line
-
hf download hf://Crusadersk/tiny-gpt2@67c8e486df0cca7c0490c621eeb5c6e624a36df7/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Crusadersk/tiny-gpt2/resolve/67c8e486df0cca7c0490c621eeb5c6e624a36df7/model.safetensors
2.51 MB
- Xet hash:
- 9d665246af8db9c85b756f1041326f4f8189919f612377bed00a2b7637199ac1
- Size of remote file:
- 2.51 MB
- SHA256:
- a4164d91c266d4d4ee381a4dcb1606df282a044ae47f7eb396a2fe11e6a21637
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