Instructions to use textattack/xlnet-base-cased-CoLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/xlnet-base-cased-CoLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="textattack/xlnet-base-cased-CoLA")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("textattack/xlnet-base-cased-CoLA") model = AutoModelForMultimodalLM.from_pretrained("textattack/xlnet-base-cased-CoLA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use textattack/xlnet-base-cased-CoLA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "textattack/xlnet-base-cased-CoLA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "textattack/xlnet-base-cased-CoLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/textattack/xlnet-base-cased-CoLA
- SGLang
How to use textattack/xlnet-base-cased-CoLA 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 "textattack/xlnet-base-cased-CoLA" \ --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": "textattack/xlnet-base-cased-CoLA", "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 "textattack/xlnet-base-cased-CoLA" \ --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": "textattack/xlnet-base-cased-CoLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use textattack/xlnet-base-cased-CoLA with Docker Model Runner:
docker model run hf.co/textattack/xlnet-base-cased-CoLA
| { | |
| "model": "xlnet-base-cased", | |
| "dataset": "glue:cola", | |
| "dataset_train_split": "train", | |
| "dataset_dev_split": "validation", | |
| "tb_writer_step": 1000, | |
| "checkpoint_steps": -1, | |
| "checkpoint_every_epoch": false, | |
| "num_train_epochs": 5, | |
| "early_stopping_epochs": -1, | |
| "batch_size": 32, | |
| "max_length": 128, | |
| "learning_rate": 3e-05, | |
| "grad_accum_steps": 1, | |
| "warmup_proportion": 0.1, | |
| "config_name": "config.json", | |
| "weights_name": "pytorch_model.bin", | |
| "enable_wandb": false, | |
| "output_dir": "/p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:cola-2020-06-29-17:32/", | |
| "num_labels": 2, | |
| "do_regression": false, | |
| "best_eval_score": 0.7976989453499521, | |
| "best_eval_score_epoch": 2, | |
| "epochs_since_best_eval_score": 2 | |
| } | |