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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("textattack/xlnet-base-cased-WNLI")
model = AutoModelForCausalLM.from_pretrained("textattack/xlnet-base-cased-WNLI")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

system HF Staff commited on Jul 6, 2020

Commit

7ae8ccd

1 Parent(s): 4e13839

Update log.txt

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+Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/log.txt.
+Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m.
+Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[0m.
+Loaded dataset. Found: 2 labels: ([0, 1])
+Loading transformers AutoModelForSequenceClassification: xlnet-base-cased
+Tokenizing training data. (len: 635)
+Tokenizing eval data (len: 71)
+Loaded data and tokenized in 8.763058185577393s
+Training model across 1 GPUs
+***** Running training *****
+	Num examples = 635
+	Batch size = 16
+	Max sequence length = 256
+	Num steps = 195
+	Num epochs = 5
+	Learning rate = 3e-05
+Eval accuracy: 57.74647887323944%
+Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/.
+Eval accuracy: 56.33802816901409%
+Eval accuracy: 45.07042253521127%
+Eval accuracy: 45.07042253521127%
+Eval accuracy: 42.25352112676056%
+Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f23225c6af0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/.
+Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/README.md.
+Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/train_args.json.