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

Libraries

How to use aelgendy/QModel with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aelgendy/QModel",
	filename="models/Qwen3-32B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use aelgendy/QModel with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

Pi new

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-server -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

Hermes Agent new

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-server -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

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

QModel

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# QModel 6 - 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

FROM python:3.11-slim

# Metadata
LABEL maintainer="QModel Team"
LABEL description="QModel v6 - Quran & Hadith RAG API"
LABEL version="4.1"

# Environment variables
ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    PIP_NO_CACHE_DIR=1

# Set working directory
WORKDIR /app

# Install system dependencies
# - build-essential: For compiling Python packages
# - 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 \
    libopenblas-dev \
    libomp-dev \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose port for API
EXPOSE 8000

# Health check
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"]