Text Generation
Transformers
PyTorch
Safetensors
gpt2
text generation
stable diffusion
midjourney
text2image
text to image
prompt augment
prompt engineering
text-generation-inference
Instructions to use pszemraj/distilgpt2-multiprompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/distilgpt2-multiprompt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pszemraj/distilgpt2-multiprompt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/distilgpt2-multiprompt") model = AutoModelForCausalLM.from_pretrained("pszemraj/distilgpt2-multiprompt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pszemraj/distilgpt2-multiprompt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pszemraj/distilgpt2-multiprompt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pszemraj/distilgpt2-multiprompt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pszemraj/distilgpt2-multiprompt
- SGLang
How to use pszemraj/distilgpt2-multiprompt 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 "pszemraj/distilgpt2-multiprompt" \ --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": "pszemraj/distilgpt2-multiprompt", "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 "pszemraj/distilgpt2-multiprompt" \ --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": "pszemraj/distilgpt2-multiprompt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pszemraj/distilgpt2-multiprompt with Docker Model Runner:
docker model run hf.co/pszemraj/distilgpt2-multiprompt
Download eval_results.json from pszemraj/distilgpt2-multiprompt: direct link, hf CLI and curl.
- Browser
- Download file 227 Bytes
-
https://huggingface.co/pszemraj/distilgpt2-multiprompt/resolve/main/eval_results.json
- Command line
-
hf download hf://pszemraj/distilgpt2-multiprompt/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/pszemraj/distilgpt2-multiprompt/resolve/main/eval_results.json
227 Bytes
| { | |
| "epoch": 2.0, | |
| "eval_loss": 2.0213370323181152, | |
| "eval_runtime": 38.4218, | |
| "eval_samples": 12974, | |
| "eval_samples_per_second": 337.672, | |
| "eval_steps_per_second": 42.216, | |
| "perplexity": 7.548410658690356 | |
| } |