Instructions to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex 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 Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
# Run inference directly in the terminal:
llama cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
# Run inference directly in the terminal:
llama cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

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 Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
# Run inference directly in the terminal:
./llama-cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

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 Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

Use Docker

docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

LM Studio
Jan

vLLM

How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

Ollama
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Ollama:
```
ollama run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
```
Unsloth Desktop
Docker Model Runner
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Docker Model Runner:
```
docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
```

Lemonade

How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex

Run and chat with the model

lemonade run user.Qwen3-4b-toolcall-gguf-llamacpp-codex-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Qwen3-4b-toolcall-gguf-llamacpp-codex

File size: 576 Bytes

812540e

version: '3.8'

services:
  qwen3-codex-server:
    build: .
    ports:
      - "8000:8000"
    volumes:
      - ./Qwen3-4B-Function-Calling-Pro.gguf:/app/Qwen3-4B-Function-Calling-Pro.gguf:ro
    environment:
      - CUDA_VISIBLE_DEVICES=""  # Disable CUDA for CPU-only mode
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s
    deploy:
      resources:
        limits:
          memory: 8G
        reservations:
          memory: 6G