LangAGI-Lab/DONUT
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How to use LangAGI-Lab/DOCTOR with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="LangAGI-Lab/DOCTOR") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("LangAGI-Lab/DOCTOR")
model = AutoModelForCausalLM.from_pretrained("LangAGI-Lab/DOCTOR")How to use LangAGI-Lab/DOCTOR with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LangAGI-Lab/DOCTOR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "LangAGI-Lab/DOCTOR",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/LangAGI-Lab/DOCTOR
How to use LangAGI-Lab/DOCTOR with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "LangAGI-Lab/DOCTOR" \
--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": "LangAGI-Lab/DOCTOR",
"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 "LangAGI-Lab/DOCTOR" \
--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": "LangAGI-Lab/DOCTOR",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use LangAGI-Lab/DOCTOR with Docker Model Runner:
docker model run hf.co/LangAGI-Lab/DOCTOR
A dialogue commonsense reasoner that generates Chain-of-Thought knowledge in a multi-hop manner given a dialogue history. Our DOCTOR is trained with DONUT which is also available on huggingface.
For more details, you can look at our paper Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents.
If you find the following model helpful, please consider citing our paper!
BibTeX:
@misc{chae2023dialogue,
title={Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents},
author={Hyungjoo Chae and Yongho Song and Kai Tzu-iunn Ong and Taeyoon Kwon and Minjin Kim and Youngjae Yu and Dongha Lee and Dongyeop Kang and Jinyoung Yeo},
year={2023},
eprint={2310.09343},
archivePrefix={arXiv},
primaryClass={cs.CL}
}