Instructions to use jianghc/medical_chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jianghc/medical_chatbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jianghc/medical_chatbot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jianghc/medical_chatbot") model = AutoModelForCausalLM.from_pretrained("jianghc/medical_chatbot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jianghc/medical_chatbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jianghc/medical_chatbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jianghc/medical_chatbot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jianghc/medical_chatbot
- SGLang
How to use jianghc/medical_chatbot 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 "jianghc/medical_chatbot" \ --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": "jianghc/medical_chatbot", "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 "jianghc/medical_chatbot" \ --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": "jianghc/medical_chatbot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jianghc/medical_chatbot with Docker Model Runner:
docker model run hf.co/jianghc/medical_chatbot
Please note that the chatbot is designed for research purposes only and is not intended for use in real medical settings. While the chatbot has been trained to provide accurate and helpful responses, it is not a substitute for professional medical advice, diagnosis, or treatment. The information provided by the chatbot should not be used to make medical decisions, and any health concerns should be addressed by a licensed healthcare provider.
Quickstart
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
path = "jianghc/medical_chatbot"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = GPT2Tokenizer.from_pretrained(path)
model = GPT2LMHeadModel.from_pretrained(path).to(device)
prompt_input = (
"The conversation between human and AI assistant.\n"
"[|Human|] {input}\n"
"[|AI|]"
)
sentence = prompt_input.format_map({'input': "what is parkinson's disease?"})
inputs = tokenizer(sentence, return_tensors="pt").to(device)
with torch.no_grad():
beam_output = model.generate(**inputs,
min_new_tokens=1,
max_length=512,
num_beams=3,
repetition_penalty=1.2,
early_stopping=True,
eos_token_id=198
)
print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
Example Outputs
The conversation between human and AI assistant.
[|Human|] what is parkinson's disease?
[|AI|] Parkinson's disease is a neurodegenerative disorder that affects movement. It is caused by the loss of dopamine-producing cells in the brain.
The conversation between human and AI assistant.
[|Human|] what type of honey is best for a bad covid cough?
[|AI|] Manuka honey has been shown to have anti-inflammatory and antibacterial properties that can help alleviate symptoms of a bad covid cough.
- Downloads last month
- 29