Instructions to use LeeChanRX/LeeChan-MedCare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeeChanRX/LeeChan-MedCare with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LeeChanRX/LeeChan-MedCare")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeeChanRX/LeeChan-MedCare")
model = AutoModelForCausalLM.from_pretrained("LeeChanRX/LeeChan-MedCare")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeeChanRX/LeeChan-MedCare with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeeChanRX/LeeChan-MedCare"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeeChanRX/LeeChan-MedCare",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeeChanRX/LeeChan-MedCare

SGLang

How to use LeeChanRX/LeeChan-MedCare 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 "LeeChanRX/LeeChan-MedCare" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeeChanRX/LeeChan-MedCare",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LeeChanRX/LeeChan-MedCare" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeeChanRX/LeeChan-MedCare",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use LeeChanRX/LeeChan-MedCare with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeeChanRX/LeeChan-MedCare to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeeChanRX/LeeChan-MedCare to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeeChanRX/LeeChan-MedCare to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LeeChanRX/LeeChan-MedCare",
    max_seq_length=2048,
)

Docker Model Runner
How to use LeeChanRX/LeeChan-MedCare with Docker Model Runner:
```
docker model run hf.co/LeeChanRX/LeeChan-MedCare
```

LeeChan-MedCare

LeeChan-MedCare is a medical conversational AI model trained for healthcare-oriented dialogue and medical assistance tasks.

Features

Medical conversation support
Symptom discussion
Healthcare guidance
Conversational medical responses

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "LeeChanRX/LeeChan-MedCare"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {
        "role": "system",
        "content": "You are LeeChan-MedCare, a helpful medical AI assistant."
    },
    {
        "role": "user",
        "content": "I have chest pain and dizziness. What could it be?"
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

outputs = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
)

response = tokenizer.decode(
    outputs[0],
    skip_special_tokens=True
)

print(response)

Disclaimer

This model is not a substitute for professional medical advice, diagnosis, or treatment.

Always consult qualified healthcare professionals for medical concerns.

Dataset

MedFlow-v1

Author

LeeChanRX

Downloads last month: 26

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for LeeChanRX/LeeChan-MedCare

Base model

LeeChanRX/LeeChanRX-3B-Instruct

Adapter

(1)

this model

Adapters

1 model

LeeChanRX
/

LeeChan-MedCare