Text Generation
Transformers
Safetensors
English
gpt2
causal-language-modeling
tinystories
scratch-trained
text-generation-inference
Instructions to use WyvernAI/gpt2-scratch-small-ftn-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WyvernAI/gpt2-scratch-small-ftn-baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WyvernAI/gpt2-scratch-small-ftn-baseline")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WyvernAI/gpt2-scratch-small-ftn-baseline") model = AutoModelForCausalLM.from_pretrained("WyvernAI/gpt2-scratch-small-ftn-baseline") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use WyvernAI/gpt2-scratch-small-ftn-baseline with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WyvernAI/gpt2-scratch-small-ftn-baseline" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WyvernAI/gpt2-scratch-small-ftn-baseline", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WyvernAI/gpt2-scratch-small-ftn-baseline
- SGLang
How to use WyvernAI/gpt2-scratch-small-ftn-baseline 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 "WyvernAI/gpt2-scratch-small-ftn-baseline" \ --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": "WyvernAI/gpt2-scratch-small-ftn-baseline", "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 "WyvernAI/gpt2-scratch-small-ftn-baseline" \ --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": "WyvernAI/gpt2-scratch-small-ftn-baseline", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WyvernAI/gpt2-scratch-small-ftn-baseline with Docker Model Runner:
docker model run hf.co/WyvernAI/gpt2-scratch-small-ftn-baseline
| { | |
| "samples": [ | |
| { | |
| "generated": "Once upon a time, there was a friendly and a shiny. They liked to show the floor. But, Lily liked to play some toys in the box on the ball, that.\n\nOne day, Anna ran", | |
| "prompt": "Once upon a time" | |
| }, | |
| { | |
| "generated": "The little robot.\n\nOne day, the little girl said, \"Thank you, Lily, Lily, we should be friends, mom. I want to play in the best for mom. This looks at the", | |
| "prompt": "The little robot" | |
| }, | |
| { | |
| "generated": "In the quiet forest was happy because she could be so happy and be so much to take a few.\n\nThe little girl felt happy, Sarah was walking over, she was a big smile on her friends had a", | |
| "prompt": "In the quiet forest" | |
| } | |
| ] | |
| } |