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

File size: 1,817 Bytes

b5e0be6

Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/log.txt.
Loading [94mnlp[0m dataset [94mglue[0m, subset [94mrte[0m, split [94mtrain[0m.
Loading [94mnlp[0m dataset [94mglue[0m, subset [94mrte[0m, split [94mvalidation[0m.
Loaded dataset. Found: 2 labels: ([0, 1])
Loading transformers AutoModelForSequenceClassification: xlnet-base-cased
Tokenizing training data. (len: 2490)
Tokenizing eval data (len: 277)
Loaded data and tokenized in 14.161188840866089s
Training model across 1 GPUs
***** Running training *****
	Num examples = 2490
	Batch size = 16
	Max sequence length = 128
	Num steps = 775
	Num epochs = 5
	Learning rate = 2e-05
Eval accuracy: 54.151624548736464%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/.
Eval accuracy: 66.06498194945848%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/.
Eval accuracy: 65.70397111913357%
Eval accuracy: 71.11913357400722%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/.
Eval accuracy: 71.11913357400722%
Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fd4a0851e80> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/.
Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/README.md.
Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:rte-2020-06-29-13:10/train_args.json.