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

Libraries

How to use binwang/xlnet-base-cased with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use binwang/xlnet-base-cased with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "binwang/xlnet-base-cased"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "binwang/xlnet-base-cased",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/binwang/xlnet-base-cased

SGLang

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

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

Historical research checkpoint — SBERT-WK (2020)

No longer actively maintained. Retained for reproducibility of the original work.

This repository hosts the original 12-layer XLNet checkpoint used in the SBERT-WK experiments. It is a supporting checkpoint for the sentence embedding method.

For the paper, original software environment, and reproduction instructions, see SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models.

Original model description

This model is pre-trained XLNET with 12 layers.

It comes with paper: SBERT-WK: A Sentence Embedding Method By Dissecting BERT-based Word Models

Project Page: SBERT-WK

Downloads last month: 189

Safetensors

Model size

0.1B params

Tensor type

F32

Collection including binwang/xlnet-base-cased

Research Archive

Collection

Historical artifacts from SBERT-WK, RSE, InstructDS, and earlier research. No longer maintained; retained for reproducibility. • 32 items • Updated 6 days ago