Instructions to use nikitaredy/medictron-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikitaredy/medictron-7B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B")
model = PeftModel.from_pretrained(base_model, "nikitaredy/medictron-7B")

Transformers

How to use nikitaredy/medictron-7B with Transformers:

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

pipe = pipeline("text-generation", model="nikitaredy/medictron-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nikitaredy/medictron-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nikitaredy/medictron-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nikitaredy/medictron-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nikitaredy/medictron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nikitaredy/medictron-7B

SGLang

How to use nikitaredy/medictron-7B 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 "nikitaredy/medictron-7B" \
    --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": "nikitaredy/medictron-7B",
		"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 "nikitaredy/medictron-7B" \
        --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": "nikitaredy/medictron-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use nikitaredy/medictron-7B with Docker Model Runner:
```
docker model run hf.co/nikitaredy/medictron-7B
```

medictron-7B / README.md

nikitaredy

Update README.md

5cc562d verified 3 months ago

3 kB

base_model: BioMistral/BioMistral-7B
library_name: peft
pipeline_tag: text-generation
tags:
  - base_model:adapter:BioMistral/BioMistral-7B
  - lora
  - sft
  - transformers
  - trl
  - unsloth
  - medical
  - indian-healthcare
  - multilingual
  - clinical-nlp
license: mit
language:
  - en
  - hi

BioMistral-7B Fine-Tuned on Indian Medical Data

A domain-adapted clinical LLM fine-tuned on synthetic Indian medical Q&A records using QLoRA (4-bit quantization) with Unsloth 2x speedup. Built to power the conversational AI layer of VitalKiosk — a contactless medical screening kiosk for senior and rural patients in India.

Research Paper

📄 VitalKiosk: An AI-Powered Contactless Medical Screening Kiosk with rPPG-Based Vital Sign Estimation and Conversational Clinical Guidance

Model Details

Developed by: B. Nikita Reddy
Model type: Causal LLM — BioMistral-7B + LoRA adapter (PEFT)
Languages: English, Hindi
License: MIT
Base model: BioMistral/BioMistral-7B

What It Does

Generates structured treatment recommendations — Allopathy, Homeopathy, Home Remedy
Supports voice input via Whisper ASR and returns spoken responses via TTS
Feeds patient vitals and symptom data into an XGBoost-based risk analyzer that scores patient risk from 0–100 (Low / Moderate / High / Critical) with SHAP explainability

Training Details

Parameter	Value
Base Model	BioMistral/BioMistral-7B
Method	QLoRA (4-bit) + Unsloth 2x speedup
Dataset	10,000 Indian medical Q&A records
Hardware	Kaggle T4 GPU (15.6 GB VRAM)
Training Time	~9.5 hours
Steps / Epochs	873 steps, 5 epochs
Parameters Trained	~0.5% (LoRA only)
Adapter Size	167.8 MB LoRA safetensors
Final Train Loss	0.065
Final Val Loss	0.163 (target < 1.0 ✅)

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B")
model = PeftModel.from_pretrained(base_model, "YOUR_HF_USERNAME/biomistral-7b-indian-medical")
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")

prompt = "Patient reports bukhar (fever) for 2 days. Suggest treatment."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Not a replacement for licensed medical advice
Optimised for Indian medical terminology — may underperform in other clinical contexts
Hindi coverage may not extend to all regional dialects
synthetic training samples — rare conditions may be underrepresented

Contact

Nikita Reddy — nikitareddywork@gmail.com

Framework Versions

PEFT 0.18.1
Unsloth (latest at training time)
Transformers (latest at training time)