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: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 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:Q4_K_M
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:Q4_K_M" } ] } } }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: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
- 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: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"
| """Centralized configuration with dual LLM backend support.""" | |
| from __future__ import annotations | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| class Config: | |
| """All settings read from environment variables with sensible defaults.""" | |
| # Backend selection | |
| LLM_BACKEND: str = os.getenv("LLM_BACKEND", "gguf") | |
| # Hugging Face backend | |
| HF_MODEL_NAME: str = os.getenv("HF_MODEL_NAME", "Qwen/Qwen3-8B") | |
| HF_DEVICE: str = os.getenv("HF_DEVICE", "auto") | |
| HF_MAX_NEW_TOKENS: int = int(os.getenv("HF_MAX_NEW_TOKENS", 2048)) | |
| # Ollama backend | |
| OLLAMA_HOST: str = os.getenv("OLLAMA_HOST", "http://localhost:11434") | |
| OLLAMA_MODEL: str = os.getenv("OLLAMA_MODEL", "qwen3.6:27b") | |
| # GGUF backend (llama-cpp-python) | |
| GGUF_MODEL_PATH: str = os.getenv("GGUF_MODEL_PATH", "./models/Qwen3.6-27B-Q4_K_M.gguf") | |
| GGUF_N_CTX: int = int(os.getenv("GGUF_N_CTX", 4096)) | |
| GGUF_N_GPU_LAYERS: int = int(os.getenv("GGUF_N_GPU_LAYERS", -1)) | |
| # LM Studio backend | |
| LMSTUDIO_URL: str = os.getenv("LMSTUDIO_URL", "http://localhost:1234") | |
| LMSTUDIO_MODEL: str = os.getenv("LMSTUDIO_MODEL", "") | |
| # Embedding model | |
| EMBED_MODEL: str = os.getenv("EMBED_MODEL", "intfloat/multilingual-e5-large") | |
| # Index & data | |
| FAISS_INDEX: str = os.getenv("FAISS_INDEX", "QModel.index") | |
| METADATA_FILE: str = os.getenv("METADATA_FILE", "metadata.json") | |
| # Retrieval | |
| TOP_K_SEARCH: int = int(os.getenv("TOP_K_SEARCH", 20)) | |
| TOP_K_RETURN: int = int(os.getenv("TOP_K_RETURN", 5)) | |
| # Generation | |
| TEMPERATURE: float = float(os.getenv("TEMPERATURE", 0.2)) | |
| MAX_TOKENS: int = int(os.getenv("MAX_TOKENS", 2048)) | |
| # Caching | |
| CACHE_SIZE: int = int(os.getenv("CACHE_SIZE", 512)) | |
| CACHE_TTL: int = int(os.getenv("CACHE_TTL", 3600)) | |
| # Ranking | |
| RERANK_ALPHA: float = float(os.getenv("RERANK_ALPHA", 0.6)) | |
| HADITH_BOOST: float = float(os.getenv("HADITH_BOOST", 0.08)) | |
| QURAN_BOOST: float = float(os.getenv("QURAN_BOOST", 0.12)) | |
| # Safety | |
| CONFIDENCE_THRESHOLD: float = float(os.getenv("CONFIDENCE_THRESHOLD", 0.30)) | |
| INTENT_THRESHOLDS: Dict[str, float] = { | |
| "auth": 0.45, # Stricter for authenticity checks | |
| "hadith": 0.30, | |
| "tafsir": 0.30, | |
| "surah_info": 0.20, # More lenient for metadata | |
| "count": 0.25, | |
| "general": 0.28, | |
| } | |
| # CORS | |
| ALLOWED_ORIGINS: str = os.getenv("ALLOWED_ORIGINS", "*") | |
| MAX_EXAMPLES: int = int(os.getenv("MAX_EXAMPLES", 3)) | |
| cfg = Config() | |