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
- llama.cpp
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:Q8_0 # Run inference directly in the terminal: llama cli -hf aelgendy/QModel:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aelgendy/QModel:Q8_0 # Run inference directly in the terminal: llama cli -hf aelgendy/QModel:Q8_0
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 aelgendy/QModel:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf aelgendy/QModel:Q8_0
Use Docker
docker model run hf.co/aelgendy/QModel:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use aelgendy/QModel with Ollama:
ollama run hf.co/aelgendy/QModel:Q8_0
- Unsloth Desktop
- Pi
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:Q8_0
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": "aelgendy/QModel:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use aelgendy/QModel with Docker Model Runner:
docker model run hf.co/aelgendy/QModel:Q8_0
- Lemonade
How to use aelgendy/QModel with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aelgendy/QModel:Q8_0
Run and chat with the model
lemonade run user.QModel-Q8_0
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:Q8_0
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:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
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:Q8_0
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:Q8_0" \ --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"
Download chat.py from aelgendy/QModel: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/aelgendy/QModel/resolve/main/chat.py
- Command line
-
hf download hf://aelgendy/QModel/chat.py
-
curl -L -o chat.py https://huggingface.co/aelgendy/QModel/resolve/main/chat.py
2.35 kB
| #!/usr/bin/env python3 | |
| """ | |
| Interactive CLI for QModel (FAISS + BM25 + Ollama/GGUF). | |
| Run directly in your terminal to query the full Islamic RAG system. | |
| """ | |
| import os | |
| import sys | |
| import json | |
| import asyncio | |
| import logging | |
| # Suppress verbose warnings | |
| os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE") | |
| os.environ.setdefault("OMP_NUM_THREADS", "1") | |
| logging.basicConfig(level=logging.WARNING) | |
| from app.config import cfg | |
| from app.state import state, _build_lookup_tables, run_rag_pipeline | |
| from app.llm import get_llm_provider | |
| from sentence_transformers import SentenceTransformer | |
| import faiss | |
| async def init_qmodel(): | |
| print("⏳ Loading QModel Islamic RAG System...") | |
| print(f" • Loading metadata: {cfg.METADATA_FILE}") | |
| with open(cfg.METADATA_FILE, "r", encoding="utf-8") as f: | |
| state.dataset = json.load(f) | |
| print(" • Building BM25 & scripture lookup tables...") | |
| _build_lookup_tables(state.dataset) | |
| print(f" • Loading dense embeddings ({cfg.EMBED_MODEL})...") | |
| state.embed_model = SentenceTransformer(cfg.EMBED_MODEL) | |
| print(f" • Loading FAISS vector index ({cfg.FAISS_INDEX})...") | |
| state.faiss_index = faiss.read_index(cfg.FAISS_INDEX) | |
| print(f" • Connecting to LLM backend ({cfg.LLM_BACKEND})...") | |
| state.llm = get_llm_provider() | |
| state.ready = True | |
| print("✅ QModel is ready!\n" + "=" * 60) | |
| async def main(): | |
| await init_qmodel() | |
| print("🕌 Sheikh QModel — Islamic Knowledge Assistant (Quran, Tafsir, Hadith, Fatwa)") | |
| print("Type your question below (or 'exit' / 'quit' to exit):\n") | |
| while True: | |
| try: | |
| query = input("Q: ").strip() | |
| except (EOFError, KeyboardInterrupt): | |
| print("\nوداعاً — مع السلامة.") | |
| break | |
| if not query: | |
| continue | |
| if query.lower() in ("exit", "quit", "q"): | |
| print("مع السلامة.") | |
| break | |
| print("\n⏳ جاري البحث في الفهرس والتحقق من النصوص...") | |
| try: | |
| res = await run_rag_pipeline(query) | |
| print("\n" + res["answer"] + "\n") | |
| print("-" * 60) | |
| except Exception as exc: | |
| print(f"❌ حدث خطأ: {exc}\n") | |
| if __name__ == "__main__": | |
| try: | |
| asyncio.run(main()) | |
| except KeyboardInterrupt: | |
| pass | |