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

Libraries

How to use NilHRH/pdband with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NilHRH/pdband",
	filename="pd_band_qwen.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 NilHRH/pdband 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 NilHRH/pdband
# Run inference directly in the terminal:
llama cli -hf NilHRH/pdband

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NilHRH/pdband
# Run inference directly in the terminal:
llama cli -hf NilHRH/pdband

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 NilHRH/pdband
# Run inference directly in the terminal:
./llama-cli -hf NilHRH/pdband

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 NilHRH/pdband
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NilHRH/pdband

Use Docker

docker model run hf.co/NilHRH/pdband

LM Studio
Jan

vLLM

How to use NilHRH/pdband with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NilHRH/pdband

Ollama
How to use NilHRH/pdband with Ollama:
```
ollama run hf.co/NilHRH/pdband
```

Unsloth Studio

How to use NilHRH/pdband 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 NilHRH/pdband 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 NilHRH/pdband to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NilHRH/pdband to start chatting

How to use NilHRH/pdband with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NilHRH/pdband

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": "NilHRH/pdband"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NilHRH/pdband with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NilHRH/pdband

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 NilHRH/pdband

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use NilHRH/pdband with Docker Model Runner:
```
docker model run hf.co/NilHRH/pdband
```

Lemonade

How to use NilHRH/pdband with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NilHRH/pdband

Run and chat with the model

lemonade run user.pdband-{{QUANT_TAG}}

List all available models

lemonade list

PD Band Explainer GGUF

PD Band is a student health-tech prototype for wearable motion monitoring. This Hugging Face repo packages the caregiver-facing explainer model and the small Python classifier artifact used by the local demo.

What This Model Does

The GGUF model explains PD Band classifier outputs in simple caregiver-facing language. It should receive structured classifier results only:

{
  "event_type": "tremor_like_movement",
  "confidence": 0.86,
  "signal_quality": "poor",
  "recent_events": 3,
  "context": "sitting"
}

The model should not receive raw IMU samples and should not be used to diagnose Parkinson's disease.

Files

pd_band_qwen.gguf: merged Qwen LoRA explainer exported to GGUF for LM Studio, Ollama, and llama.cpp-style runtimes.
pd_band_classifier.pkl: scikit-learn prototype classifier used by the Python pipeline.
qwen_lora_adapter/: PEFT LoRA adapter files.
lm_studio_system_prompt.txt: recommended system prompt for LM Studio.
ollama.Modelfile: Ollama template used for the public Ollama model.
sample_prompt.json: example classifier result payload.
reports/: training, metrics, and safety notes from the project.

LM Studio Use

Download pd_band_qwen.gguf.
Open LM Studio and load the GGUF as a local model.
Paste lm_studio_system_prompt.txt as the system prompt.
Ask with a classifier-result JSON, not raw sensor data.

Example prompt:

Explain this PD Band classifier result to a caregiver in simple, safe language.

{
  "event_type": "possible_freezing_of_gait",
  "confidence": 0.52,
  "signal_quality": "good",
  "recent_events": 1,
  "context": "walking"
}

Ollama Use

Ollama thinking output should be disabled for this model:

ollama run Nilabh_yadav/pdband:latest --think=false

Inside an existing Ollama chat:

/set nothink

Safety

This is not a medical device, diagnostic model, medication advisor, or emergency service. It is a student prototype for logging and caregiver review.

The classifier metrics were produced on synthetic IMU windows. High synthetic accuracy is only pipeline verification, not clinical validation.

Downloads last month: 212

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants