Instructions to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF", dtype="auto")

llama-cpp-python

How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF",
	filename="unsloth.F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF 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 Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

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 Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

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 Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

Use Docker

docker model run hf.co/Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

LM Studio
Jan
Ollama
How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with Ollama:
```
ollama run hf.co/Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
```

Unsloth Studio

How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF 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 Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF 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 Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with Docker Model Runner:
```
docker model run hf.co/Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16
```

Lemonade

How to use Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-4B-Python-Combined-v2_GGUF-F16

List all available models

lemonade list

Uploaded model

Developed by: Montecarlo2024
License: apache-2.0
Finetuned from model : unsloth/qwen3-4b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

System Prompt: You should include the following in your system prompt for this model
SYSTEM """You are a helpful assistant. Respond directly to user questions without repeating the question or generating additional user prompts.""" _

Downloads last month: 8

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Montecarlo2024
/

Qwen3-4B-Python-Combined-v2_GGUF

Uploaded model

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Dataset used to train Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF