Instructions to use voidful/bart-distractor-generation-pm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use voidful/bart-distractor-generation-pm with Transformers:

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

pipe = pipeline("text-generation", model="voidful/bart-distractor-generation-pm")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("voidful/bart-distractor-generation-pm")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/bart-distractor-generation-pm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use voidful/bart-distractor-generation-pm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/voidful/bart-distractor-generation-pm

SGLang

How to use voidful/bart-distractor-generation-pm 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 "voidful/bart-distractor-generation-pm" \
    --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": "voidful/bart-distractor-generation-pm",
		"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 "voidful/bart-distractor-generation-pm" \
        --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": "voidful/bart-distractor-generation-pm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use voidful/bart-distractor-generation-pm with Docker Model Runner:
```
docker model run hf.co/voidful/bart-distractor-generation-pm
```

bart-distractor-generation-pm

Commit History

Mar 21, 2023

11:55
Adding `safetensors` variant of this model⁠

SFconvertbot

095c616

Apr 4, 2021

16:20
update model and readme

voidful

ab4250c

Mar 23, 2021

14:39
init

voidful

28a908d
14:28
initial commit

system HF Staff

bb4b7f0

Commit History

Mar 21, 2023

Adding `safetensors` variant of this model⁠

Apr 4, 2021

update model and readme

Mar 23, 2021

init

initial commit