Text Generation
Transformers
PyTorch
English
bart
text2text-generation
question generation
Eval Results (legacy)
Instructions to use research-backup/bart-large-squad-qg-default with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use research-backup/bart-large-squad-qg-default with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="research-backup/bart-large-squad-qg-default")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("research-backup/bart-large-squad-qg-default") model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/bart-large-squad-qg-default") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use research-backup/bart-large-squad-qg-default with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "research-backup/bart-large-squad-qg-default" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "research-backup/bart-large-squad-qg-default", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/research-backup/bart-large-squad-qg-default
- SGLang
How to use research-backup/bart-large-squad-qg-default 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 "research-backup/bart-large-squad-qg-default" \ --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": "research-backup/bart-large-squad-qg-default", "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 "research-backup/bart-large-squad-qg-default" \ --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": "research-backup/bart-large-squad-qg-default", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use research-backup/bart-large-squad-qg-default with Docker Model Runner:
docker model run hf.co/research-backup/bart-large-squad-qg-default
| {"dataset_path": "lmqg/qg_squad", "dataset_name": "default", "input_types": ["paragraph_answer"], "output_types": ["question"], "prefix_types": null, "model": "facebook/bart-large", "max_length": 512, "max_length_output": 32, "epoch": 10, "batch": 8, "lr": 1.25e-05, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 4, "label_smoothing": 0.1} |