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
| { | |
| "architectures": [ | |
| "XLNetLMHeadModel" | |
| ], | |
| "attn_type": "bi", | |
| "bi_data": false, | |
| "bos_token_id": 1, | |
| "clamp_len": -1, | |
| "d_head": 64, | |
| "d_inner": 3072, | |
| "d_model": 768, | |
| "dropout": 0.1, | |
| "end_n_top": 5, | |
| "eos_token_id": 2, | |
| "ff_activation": "gelu", | |
| "finetuning_task": "glue:cola", | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "mem_len": null, | |
| "model_type": "xlnet", | |
| "n_head": 12, | |
| "n_layer": 12, | |
| "pad_token_id": 5, | |
| "reuse_len": null, | |
| "same_length": false, | |
| "start_n_top": 5, | |
| "summary_activation": "tanh", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "last", | |
| "summary_use_proj": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 250 | |
| } | |
| }, | |
| "untie_r": true, | |
| "vocab_size": 32000 | |
| } | |