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

Libraries

How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex",
	filename="Qwen3-4B-Function-Calling-Pro.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

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 Studio

How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex 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 Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex 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 Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex to start chatting

Atomic Chat new
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

Qwen3-4b-toolcall-gguf-llamacpp-codex / setup.py

Manojb

Upload folder using huggingface_hub

812540e verified 10 months ago

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	#!/usr/bin/env python3
	"""
	Setup script for Qwen3-4B Tool Calling with llama-cpp-python
	"""

	from setuptools import setup, find_packages

	with open("README.md", "r", encoding="utf-8") as fh:
	long_description = fh.read()

	with open("requirements.txt", "r", encoding="utf-8") as fh:
	requirements = [line.strip() for line in fh if line.strip() and not line.startswith("#")]

	setup(
	name="qwen3-4b-toolcall-llamacpp",
	version="1.0.0",
	author="Manojb",
	author_email="your.email@example.com",
	description="Qwen3-4B model fine-tuned for tool calling with llama-cpp-python",
	long_description=long_description,
	long_description_content_type="text/markdown",
	url="https://github.com/yourusername/qwen3-4b-toolcall-llamacpp",
	packages=find_packages(),
	classifiers=[
	"Development Status :: 4 - Beta",
	"Intended Audience :: Developers",
	"License :: OSI Approved :: Apache Software License",
	"Operating System :: OS Independent",
	"Programming Language :: Python :: 3",
	"Programming Language :: Python :: 3.8",
	"Programming Language :: Python :: 3.9",
	"Programming Language :: Python :: 3.10",
	"Programming Language :: Python :: 3.11",
	"Programming Language :: Python :: 3.12",
	"Topic :: Scientific/Engineering :: Artificial Intelligence",
	"Topic :: Software Development :: Libraries :: Python Modules",
	],
	python_requires=">=3.8",
	install_requires=requirements,
	extras_require={
	"dev": [
	"pytest>=7.0.0",
	"black>=22.0.0",
	"flake8>=5.0.0",
	],
	},
	include_package_data=True,
	package_data={
	"": [".gguf", ".json", "*.jinja"],
	},
	)