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 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
- 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 @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
File size: 8,239 Bytes
7ecdf4a 20edea9 15f1210 20edea9 15f1210 20edea9 15f1210 20edea9 15f1210 20edea9 eb1414a 20edea9 15f1210 20edea9 15f1210 20edea9 15f1210 20edea9 15f1210 20edea9 15f1210 20edea9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | # QModel 6 Configuration Template
# ==================================
# Copy this to .env and update values for your environment
# LLM Backend Selection
# Options: "gguf" (local GGUF file), "ollama", "hf" (HuggingFace), or "lmstudio"
LLM_BACKEND=gguf
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# GGUF BACKEND (if LLM_BACKEND=gguf) β self-contained, no external daemon
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
GGUF_MODEL_PATH=./models/Qwen3.6-27B-Q4_K_M.gguf
GGUF_N_CTX=4096 # Context window size
GGUF_N_GPU_LAYERS=-1 # -1 = offload all layers to GPU (Metal on Mac)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# OLLAMA BACKEND (if LLM_BACKEND=ollama)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# OLLAMA_HOST=http://localhost:11434
# OLLAMA_MODEL=qwen3.6:27b
# Available models: llama3.1, mistral, neural-chat, openhermes
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# HUGGINGFACE BACKEND (if LLM_BACKEND=hf)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# HF_MODEL_NAME=Qwen/Qwen3-8B
# HF_DEVICE=auto # Options: auto, cuda, cpu
# HF_MAX_NEW_TOKENS=2048
# Popular models:
# - Qwen/Qwen3-8B (excellent Arabic)
# - mistralai/Mistral-7B-Instruct-v0.2
# - meta-llama/Llama-2-13b-chat-hf
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# LM STUDIO BACKEND (if LLM_BACKEND=lmstudio)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# LMSTUDIO_URL=http://localhost:1234
# LMSTUDIO_MODEL=qwen2.5-7b-instruct # Model loaded in LM Studio
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# EMBEDDING MODEL (shared by all backends)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
EMBED_MODEL=intfloat/multilingual-e5-large
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# DATA FILES
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
FAISS_INDEX=QModel.index
METADATA_FILE=metadata.json
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# RETRIEVAL SETTINGS
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
TOP_K_SEARCH=20 # Candidate pool size
TOP_K_RETURN=5 # Final results returned to user
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# GENERATION SETTINGS
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
TEMPERATURE=0.2 # 0.0=deterministic, 1.0=creative
MAX_TOKENS=2048 # Max output length
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# SAFETY & QUALITY
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Confidence threshold: Below this score, skip LLM and return "not found"
# Prevents hallucinations but may miss valid results
# Range: 0.0-1.0 (default 0.30)
# Tune up (0.50+) for stricter, tune down (0.20) for looser
CONFIDENCE_THRESHOLD=0.30
# Hadith boost: Score bonus when intent=hadith
# Prevents Quran verses from outranking relevant Hadiths
HADITH_BOOST=0.08
# Quran boost: Score bonus when intent=tafsir/quran
# Prevents Hadiths from outranking relevant Quran verses
QURAN_BOOST=0.12
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# RANKING
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
RERANK_ALPHA=0.6 # 60% dense (embedding), 40% sparse (BM25)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# CACHING
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
CACHE_SIZE=512 # Max cache entries
CACHE_TTL=3600 # Cache expiry in seconds
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# SECURITY
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
ALLOWED_ORIGINS=* # CORS origins (restrict in production: origin1.com,origin2.com)
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# USAGE EXAMPLES
# βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
#
# Self-contained (GGUF, e.g. HF Spaces):
# LLM_BACKEND=gguf
# GGUF_MODEL_PATH=./models/Qwen3.6-27B-Q4_K_M.gguf
#
# Development (Ollama):
# LLM_BACKEND=ollama
# OLLAMA_HOST=http://localhost:11434
# OLLAMA_MODEL=qwen3.6:27b
#
# Production (HuggingFace GPU):
# LLM_BACKEND=hf
# HF_MODEL_NAME=Qwen/Qwen3-8B
# HF_DEVICE=cuda
#
# Production (HuggingFace CPU):
# LLM_BACKEND=hf
# HF_MODEL_NAME=Qwen/Qwen3-8B
# HF_DEVICE=cpu
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