Text Generation
Safetensors
GGUF
English
qwen3
function-calling
tool-calling
codex
local-llm
4gb-vram
llama-cpp
code-assistant
api-tools
openai-alternative
qwen
instruct
conversational
custom_code
8-bit precision
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
- llama.cpp
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
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