OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
Paper • 2309.10706 • Published • 17
How to use OpenNLG/OpenBA-V1-Flan with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="OpenNLG/OpenBA-V1-Flan", trust_remote_code=True) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenNLG/OpenBA-V1-Flan", trust_remote_code=True, dtype="auto")How to use OpenNLG/OpenBA-V1-Flan with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenNLG/OpenBA-V1-Flan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "OpenNLG/OpenBA-V1-Flan",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/OpenNLG/OpenBA-V1-Flan
How to use OpenNLG/OpenBA-V1-Flan with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "OpenNLG/OpenBA-V1-Flan" \
--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": "OpenNLG/OpenBA-V1-Flan",
"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 "OpenNLG/OpenBA-V1-Flan" \
--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": "OpenNLG/OpenBA-V1-Flan",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use OpenNLG/OpenBA-V1-Flan with Docker Model Runner:
docker model run hf.co/OpenNLG/OpenBA-V1-Flan
OpenBA is an open-source bilingual language model equipped with 15 billion parameters, built on the T5 architecture.
We are excited to unveil two distinguished versions of our model, with another on the horizon:
pip install transformers torch>=2.0 sentencepiece
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("OpenBA/OpenBA-Flan", trust_remote_code=True)
>>> model = AutoModelForSeq2SeqLM.from_pretrained("OpenBA/OpenBA-Flan", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> query = "<S>" + "介绍一下中国的四大名著,并分别概括其主要内容" + "<extra_id_0>"
>>> inputs = tokenizer(query, return_tensors="pt").to("cuda")
>>> outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> print(response)
中国的四大名著分别是《红楼梦》、《西游记》、《水浒传》和《三国演义》。它们分别包括故事情节、文化内涵和历史背景等方面的不同特点。《红楼梦》是一部中国古典小说,讲述了贾宝玉、林黛玉、薛宝钗等一群人物在贾府的生活和爱情故事。《西游记》是中国著名小说,描述了孙悟空、猪八戒、沙悟净等一众妖魔鬼怪的冒险历程和故事。《水浒传》是一部中国古典小说,描述了宋江等一百零八位好汉的反抗故事。《三国演义》是中国古代著名小说,讲述了三国时期的历史和战争故事。这些小说在文学、历史、哲学和文化等方面都有着不同的影响和地位。
docker model run hf.co/OpenNLG/OpenBA-V1-Flan