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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aelgendy/QModel 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 aelgendy/QModel:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aelgendy/QModel:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aelgendy/QModel:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aelgendy/QModel:Q4_K_M

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 aelgendy/QModel:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aelgendy/QModel:Q4_K_M

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 aelgendy/QModel:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aelgendy/QModel:Q4_K_M

Use Docker

docker model run hf.co/aelgendy/QModel:Q4_K_M

LM Studio
Jan
Ollama
How to use aelgendy/QModel with Ollama:
```
ollama run hf.co/aelgendy/QModel:Q4_K_M
```

Unsloth Studio

How to use aelgendy/QModel 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 aelgendy/QModel 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 aelgendy/QModel to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use aelgendy/QModel with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aelgendy/QModel:Q4_K_M

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": "aelgendy/QModel:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use aelgendy/QModel with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aelgendy/QModel:Q4_K_M

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 "aelgendy/QModel:Q4_K_M" \
  --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 aelgendy/QModel with Docker Model Runner:
```
docker model run hf.co/aelgendy/QModel:Q4_K_M
```

Lemonade

How to use aelgendy/QModel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aelgendy/QModel:Q4_K_M

Run and chat with the model

lemonade run user.QModel-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use aelgendy/QModel with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aelgendy/QModel:Q4_K_M

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 aelgendy/QModel:Q4_K_M

Run Hermes

hermes

Atomic Chat

QModel / Dockerfile

aelgendy

Sync codebase with local main (config, README, docker, gitignore updates)

15f1210 verified 11 days ago

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	# QModel 7 - Islamic RAG API
	# =============================
	# Dockerfile for QModel API
	# Supports both Ollama and HuggingFace backends via .env configuration
	#
	# Build: docker build -t qmodel .
	# Run: docker run -p 8000:8000 --env-file .env qmodel

	# ── Builder stage: compile deps, keep toolchain out of the final image ──
	FROM python:3.11-slim AS builder

	ENV PYTHONDONTWRITEBYTECODE=1 \
	PYTHONUNBUFFERED=1 \
	PIP_NO_CACHE_DIR=1

	# - build-essential/cmake: for compiling llama-cpp-python and other native deps
	# - libopenblas-dev: for numerical operations (FAISS, numpy)
	# - libomp-dev: for OpenMP (FAISS parallelization)
	RUN apt-get update && apt-get install -y --no-install-recommends \
	build-essential \
	cmake \
	libopenblas-dev \
	libomp-dev \
	&& rm -rf /var/lib/apt/lists/*

	RUN python -m venv /opt/venv
	ENV PATH="/opt/venv/bin:$PATH"

	COPY requirements.txt .

	# faiss-cpu means this deployment doesn't use GPU acceleration — install the
	# CPU-only torch wheel first so requirements.txt doesn't pull the default
	# CUDA-enabled build (several GB of unused NVIDIA libraries).
	RUN pip install --default-timeout=1000 --retries 10 --no-cache-dir \
	--extra-index-url https://download.pytorch.org/whl/cpu torch \
	&& pip install --default-timeout=1000 --retries 10 --no-cache-dir -r requirements.txt

	# ── Final stage: slim runtime image ─────────────────────────────────────
	FROM python:3.11-slim

	LABEL author="Abdullah Elgendy"
	LABEL maintainer="Abdullah Elgendy"
	LABEL description="QModel v7 - Quran & Hadith RAG API"
	LABEL version="7.0.0"

	ENV PYTHONDONTWRITEBYTECODE=1 \
	PYTHONUNBUFFERED=1 \
	PATH="/opt/venv/bin:$PATH"

	WORKDIR /app

	# Runtime-only system deps (no compiler toolchain here)
	RUN apt-get update && apt-get install -y --no-install-recommends \
	libopenblas0-pthread \
	libomp5 \
	curl \
	&& rm -rf /var/lib/apt/lists/*

	COPY --from=builder /opt/venv /opt/venv

	# Copy application code
	COPY . .

	# Run as non-root
	RUN useradd --create-home --uid 1000 appuser \
	&& chown -R appuser:appuser /app
	USER appuser

	# Expose port for API
	EXPOSE 8000

	# Health check — /health returns 503 until the RAG pipeline finishes loading
	HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
	CMD curl -f http://localhost:8000/health \|\| exit 1

	# Start application
	# Configure via .env: LLM_BACKEND=ollama or LLM_BACKEND=hf
	CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]