An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models
Paper • 2110.08527 • Published
How to use danj0nes/dropout_gpt2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="danj0nes/dropout_gpt2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("danj0nes/dropout_gpt2")
model = AutoModelForCausalLM.from_pretrained("danj0nes/dropout_gpt2")How to use danj0nes/dropout_gpt2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danj0nes/dropout_gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "danj0nes/dropout_gpt2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/danj0nes/dropout_gpt2
How to use danj0nes/dropout_gpt2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "danj0nes/dropout_gpt2" \
--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": "danj0nes/dropout_gpt2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "danj0nes/dropout_gpt2" \
--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": "danj0nes/dropout_gpt2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use danj0nes/dropout_gpt2 with Docker Model Runner:
docker model run hf.co/danj0nes/dropout_gpt2
This model is a fine-tuned version of gpt2 on a English Wikipedia dataset.
Dropout debiased gpt2 using the hyperparameters specified in Measuring and Reducing Gendered Correlations in Pre-trained Models (Webster et al. 2021) and the code used in An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models (Meade et al. 2022).
The following hyperparameters were used during training:
Base model
openai-community/gpt2