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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- 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.'
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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.
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For more details, you can look at our paper (https://arxiv.org/abs/2310.09343).
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- 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.'
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- example_title: 'example 1'
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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.
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For more details, you can look at our paper (https://arxiv.org/abs/2310.09343).
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