Instructions to use ayureasehealthcare/ayurezeastraai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayureasehealthcare/ayurezeastraai with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ayureasehealthcare/ayurezeastraai", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayureasehealthcare/ayurezeastraai with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ayureasehealthcare/ayurezeastraai

SGLang

How to use ayureasehealthcare/ayurezeastraai 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 "ayureasehealthcare/ayurezeastraai" \
    --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": "ayureasehealthcare/ayurezeastraai",
		"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 "ayureasehealthcare/ayurezeastraai" \
        --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": "ayureasehealthcare/ayurezeastraai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayureasehealthcare/ayurezeastraai with Docker Model Runner:
```
docker model run hf.co/ayureasehealthcare/ayurezeastraai
```

ayurezeastraai / README.md

ayureasehealthcare

Update README.md

c4e2b9c verified about 1 year ago

1.13 kB

license: apache-2.0
language:
  - en
tags:
  - medgemma
  - ayurveda
  - healthcare
  - instruction-tuning
  - lora
  - 4bit
datasets:
  - Macromrit/ayurveda-text-based-qanda
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: MedGemma Ayurvedic LoRA
    results: []

MedGemma 4B - LoRA Fine-tuned on Ayurveda Q&A

This model is a fine-tuned version of google/medgemma-4b-it using LoRA (Low-Rank Adaptation) in 4-bit precision, trained on the Macromrit/ayurveda-text-based-qanda dataset.
It specializes in answering healthcare and Ayurvedic medical questions in an instruction-following format.

🧠 Model Details

Base model: google/medgemma-4b-it
Fine-tuned with: LoRA + 4-bit quantization
Training data: Macromrit/ayurveda-text-based-qanda
Task: Instruction-tuned text generation for Ayurveda Q&A
Language: English
License: Apache 2.0