Instructions to use textattack/xlnet-base-cased-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use textattack/xlnet-base-cased-RTE with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="textattack/xlnet-base-cased-RTE")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textattack/xlnet-base-cased-RTE")
model = AutoModelForCausalLM.from_pretrained("textattack/xlnet-base-cased-RTE", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use textattack/xlnet-base-cased-RTE 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-RTE"
# 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-RTE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/textattack/xlnet-base-cased-RTE

SGLang

How to use textattack/xlnet-base-cased-RTE 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-RTE" \
    --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-RTE",
		"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-RTE" \
        --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-RTE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use textattack/xlnet-base-cased-RTE with Docker Model Runner:
```
docker model run hf.co/textattack/xlnet-base-cased-RTE
```

xlnet-base-cased-RTE

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{
  "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:rte",
  "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
}