Instructions to use Rustamshry/Medical-QA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rustamshry/Medical-QA-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rustamshry/Medical-QA-GGUF",
	filename="model.f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Rustamshry/Medical-QA-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Rustamshry/Medical-QA-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Rustamshry/Medical-QA-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Rustamshry/Medical-QA-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Rustamshry/Medical-QA-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rustamshry/Medical-QA-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Rustamshry/Medical-QA-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rustamshry/Medical-QA-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rustamshry/Medical-QA-GGUF:F16

Use Docker

docker model run hf.co/Rustamshry/Medical-QA-GGUF:F16

LM Studio
Jan

vLLM

How to use Rustamshry/Medical-QA-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rustamshry/Medical-QA-GGUF:F16

Ollama
How to use Rustamshry/Medical-QA-GGUF with Ollama:
```
ollama run hf.co/Rustamshry/Medical-QA-GGUF:F16
```

Unsloth Studio

How to use Rustamshry/Medical-QA-GGUF 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 Rustamshry/Medical-QA-GGUF 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 Rustamshry/Medical-QA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rustamshry/Medical-QA-GGUF to start chatting

How to use Rustamshry/Medical-QA-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rustamshry/Medical-QA-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Rustamshry/Medical-QA-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Rustamshry/Medical-QA-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rustamshry/Medical-QA-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Rustamshry/Medical-QA-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Rustamshry/Medical-QA-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rustamshry/Medical-QA-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Rustamshry/Medical-QA-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Rustamshry/Medical-QA-GGUF with Docker Model Runner:
```
docker model run hf.co/Rustamshry/Medical-QA-GGUF:F16
```

Lemonade

How to use Rustamshry/Medical-QA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rustamshry/Medical-QA-GGUF:F16

Run and chat with the model

lemonade run user.Medical-QA-GGUF-F16

List all available models

lemonade list

Model Card for Medical-QA

Model Details

GGUF version of https://huggingface.co/khazarai/Medical-QA

This model is a fine-tuned version of Qwen3-0.6B on a 34K medical Q&A dataset derived from the Anki Medical Curriculum flashcards. It is designed to assist with medical education and exam preparation, offering concise and contextually relevant answers to short medical questions.

Base Model: Qwen3-0.6B
Fine-tuned on: 34,000 question-answer pairs
Domain: Medicine & Medical Education
Languages: English
License: MIT

Uses

Direct Use

Primary use case: Medical Q&A for students, exam preparation, and knowledge review.
Suitable for interactive learning assistants or educational chatbots.
Not intended for real-world clinical decision-making or replacing professional medical advice.

Bias, Risks, and Limitations

The model’s knowledge is constrained to the dataset scope (flashcard-style Q&A).
Responses are short and exam-style rather than detailed clinical explanations.
Should not be relied upon for actual patient care, treatment decisions, or emergency use.

Training Data

The dataset is based on Anki Medical Curriculum flashcards, created and updated by medical students. These flashcards cover the entire medical curriculum, including but not limited to: