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
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A dialogue commonsense reasoner that generates Chain-of-Thought knowledge in a multi-hop manner. Our DOCTOR is trained with DONUT(https://huggingface.co/datasets/DLI-Lab/DONUT) which is also available on huggingface.
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For more details, you can look at our paper (https://arxiv.org/abs/2310.09343).
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A dialogue commonsense reasoner that generates Chain-of-Thought knowledge in a multi-hop manner. Our DOCTOR is trained with DONUT(https://huggingface.co/datasets/DLI-Lab/DONUT) which is also available on huggingface.
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For more details, you can look at our paper (https://arxiv.org/abs/2310.09343).
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If you find the following model helpful, please consider citing our paper!
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**BibTeX:**
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```bibtex
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@misc{chae2023dialogue,
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title={Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents},
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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},
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year={2023},
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eprint={2310.09343},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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