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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles") model = AutoModelForCausalLM.from_pretrained("DunnBC22/distilgpt2-CLM_US_Economic_News_Articles") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
- Xet hash:
- 6f79672c15bd66842e4bcad1f3564073976d1b41fcb11bd02e4e0a21f97b49af
- Size of remote file:
- 334 MB
- SHA256:
- e596f383420c4603a14ead64315b2e52792a6174791748504dee08360f6ca33c
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