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 / .env.example

aelgendy

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

15f1210 verified 15 days ago

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	# 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

	# ─────────────────────────────────────────────────────────────────────
	# GGUF BACKEND (if LLM_BACKEND=gguf) — self-contained, no external daemon
	# ─────────────────────────────────────────────────────────────────────
	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)

	# ─────────────────────────────────────────────────────────────────────
	# OLLAMA BACKEND (if LLM_BACKEND=ollama)
	# ─────────────────────────────────────────────────────────────────────
	# OLLAMA_HOST=http://localhost:11434
	# OLLAMA_MODEL=qwen3.6:27b
	# Available models: llama3.1, mistral, neural-chat, openhermes

	# ─────────────────────────────────────────────────────────────────────
	# HUGGINGFACE BACKEND (if LLM_BACKEND=hf)
	# ─────────────────────────────────────────────────────────────────────
	# 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

	# ─────────────────────────────────────────────────────────────────────
	# LM STUDIO BACKEND (if LLM_BACKEND=lmstudio)
	# ─────────────────────────────────────────────────────────────────────
	# LMSTUDIO_URL=http://localhost:1234
	# LMSTUDIO_MODEL=qwen2.5-7b-instruct # Model loaded in LM Studio

	# ─────────────────────────────────────────────────────────────────────
	# EMBEDDING MODEL (shared by all backends)
	# ─────────────────────────────────────────────────────────────────────
	EMBED_MODEL=intfloat/multilingual-e5-large

	# ─────────────────────────────────────────────────────────────────────
	# DATA FILES
	# ─────────────────────────────────────────────────────────────────────
	FAISS_INDEX=QModel.index
	METADATA_FILE=metadata.json

	# ─────────────────────────────────────────────────────────────────────
	# RETRIEVAL SETTINGS
	# ─────────────────────────────────────────────────────────────────────
	TOP_K_SEARCH=20 # Candidate pool size
	TOP_K_RETURN=5 # Final results returned to user

	# ─────────────────────────────────────────────────────────────────────
	# GENERATION SETTINGS
	# ─────────────────────────────────────────────────────────────────────
	TEMPERATURE=0.2 # 0.0=deterministic, 1.0=creative
	MAX_TOKENS=2048 # Max output length

	# ─────────────────────────────────────────────────────────────────────
	# SAFETY & QUALITY
	# ─────────────────────────────────────────────────────────────────────
	# 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

	# ─────────────────────────────────────────────────────────────────────
	# RANKING
	# ─────────────────────────────────────────────────────────────────────
	RERANK_ALPHA=0.6 # 60% dense (embedding), 40% sparse (BM25)

	# ─────────────────────────────────────────────────────────────────────
	# CACHING
	# ─────────────────────────────────────────────────────────────────────
	CACHE_SIZE=512 # Max cache entries
	CACHE_TTL=3600 # Cache expiry in seconds

	# ─────────────────────────────────────────────────────────────────────
	# SECURITY
	# ─────────────────────────────────────────────────────────────────────
	ALLOWED_ORIGINS=* # CORS origins (restrict in production: origin1.com,origin2.com)

	# ─────────────────────────────────────────────────────────────────────
	# USAGE EXAMPLES
	# ─────────────────────────────────────────────────────────────────────
	#
	# 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