Text Generation
Transformers
PyTorch
TensorBoard
English
gpt2
Generated from Trainer
text-generation-inference
Instructions to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DunnBC22/distilgpt2-CLM_US_Economic_News_Articles")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles") model = AutoModelForMultimodalLM.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DunnBC22/distilgpt2-CLM_US_Economic_News_Articles
- SGLang
How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles 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 "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles" \ --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": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles", "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 "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles" \ --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": "DunnBC22/distilgpt2-CLM_US_Economic_News_Articles", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DunnBC22/distilgpt2-CLM_US_Economic_News_Articles with Docker Model Runner:
docker model run hf.co/DunnBC22/distilgpt2-CLM_US_Economic_News_Articles
distilgpt2-CLM_US_Economic_News_Articles / runs /Mar07_21-22-36_Brians-Mac-mini /events.out.tfevents.1678333366.Brians-Mac-mini.10438.2
- Xet hash:
- a9268ea2afbbadfa2f03e2f1f12b709b2508ce8b0ba6d9286ad72e5c7dda0334
- Size of remote file:
- 311 Bytes
- SHA256:
- 051875a1d6086c603037ca146f566b604e0225043994d58543db03f3ed30492a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.