Instructions to use LangAGI-Lab/DOCTOR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LangAGI-Lab/DOCTOR with vLLM:
Install from pip and serve model
# 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 }'Use Docker
docker model run hf.co/LangAGI-Lab/DOCTOR
- SGLang
How to use LangAGI-Lab/DOCTOR 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 "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 }'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 "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 Model Runner
How to use LangAGI-Lab/DOCTOR with Docker Model Runner:
docker model run hf.co/LangAGI-Lab/DOCTOR
| license: apache-2.0 | |
| datasets: | |
| - DLI-Lab/DONUT | |
| widget: | |
| - text: 'A: Hi, Viggo. How are you doing today?\nB: Hey, Yovani. I’m doing all right. Thanks for asking.\nA: No problem. I saw that you left your coffee mug on the counter this morning. Did you forget to take it with you?\nB: Yeah, I did. Thanks for grabbing it for me.\nA: No problem at all. I know how busy you are and I didn’t want you to have to come back for it later.\nB: You’re a lifesaver, Yovani. Seriously, thank you so much.' | |
| - example_title: 'example 1' | |
| 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](https://huggingface.co/datasets/DLI-Lab/DONUT) which is also available on huggingface. | |
| ## Links for Reference | |
| - **Demo:https://dialoguecot.web.app/** | |
| - **Repository:https://github.com/kyle8581/DialogueCoT** | |
| - **Paper:https://arxiv.org/abs/2310.09343** | |
| - **Point of Contact:mapoout@yonsei.ac.kr** | |
|  | |
| For more details, you can look at our paper [Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents](https://arxiv.org/abs/2310.09343). | |
| If you find the following model helpful, please consider citing our paper! | |
| **BibTeX:** | |
| ```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} | |
| } | |
| ``` |