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

OpenClaw new

How to use NilHRH/pdband with OpenClaw:

Start the llama.cpp server

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

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 "NilHRH/pdband" \
  --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 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

NilHRH commited on 30 days ago

Commit

2a41b2e

verified ·

1 Parent(s): fbb5ccc

Upload reports/limitations_and_safety.md with huggingface_hub

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+# Limitations and Safety
+## Current Status
+This project is a student prototype. The verified default pipeline uses synthetic IMU windows to test feature extraction, model training, model saving, evaluation, and demos.
+## Major Limitations
+- Synthetic data does not represent the full variability of real Parkinson's symptoms, sensor placement, device drift, age, gait style, medication state, or home environments.
+- The four labels are prototype event categories, not clinical diagnoses.
+- The classifier is trained on short windows and does not understand patient history unless those features are added later.
+- Public FoG datasets often cover normal versus FoG-like movement and may not include tremor or risky/unusual movement classes.
+- Kaggle FoG data is accelerometer-only in common competition files, so gyroscope channels may be unavailable.
+- High synthetic accuracy should be described only as pipeline verification.
+## Responsible Use
+- Do not use this model to diagnose Parkinson's disease.
+- Do not use this model to change medication, therapy, or cueing plans.
+- Repeated concerning logs should be shared with a caregiver or qualified clinician.
+- A risky/unusual movement alert should prompt a welfare check. Emergency action should depend on observed injury, fall, confusion, or unresponsiveness.
+- The current alarm system is local-only. It records alarm events to a JSONL file and prints them in demos; it does not send SMS, email, push notifications, or emergency calls.
+- The report-history chat answers from saved logs only. It should not be treated as a clinician, and it should not infer diagnoses from incomplete history.
+## Next Validation Steps
+- Collect consented real wearable data with consistent sensor placement and clinical annotation.
+- Validate across different users and recording sessions.
+- Report sensitivity, specificity, false alert rate, and missed-event rate by event class.
+- Test calibration and signal-quality rejection.
+- Review all user-facing text with a clinician before real-world use.