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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

Rustamshry commited on Apr 8

Commit

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1 Parent(s): 4c49017

Update README.md

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@@ -11,8 +11,48 @@ tags:
 - sft
 ---
-***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Bio-8B-it-GGUF).***
-weighted/imatrix quants are available at https://huggingface.co/mradermacher/Bio-8B-it-i1-GGUF

 - sft
 ---
+# 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:
+- Anatomy
+- Physiology
+- Pathology
+- Pharmacology
+- Clinical knowledge and skills
+The flashcards typically provide succinct summaries and mnemonics to support learning and retention.