Instructions to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with Ollama:
```
ollama run hf.co/NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
```

Unsloth Studio

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF to start chatting

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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": "NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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 "NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF: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 NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M
```

Lemonade

How to use NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-4B-Mathematical-Thinking-GGUF-Q4_K_M

List all available models

lemonade list

🧮 Qwen-4B Mathematical Thinking (GGUF)

Base model: unsloth/Qwen3-4B-unsloth-bnb-4bit Quantization: Q4_K_M (GGUF) Language: English License: Apache-2.0

✨ Overview

This is a fine-tuned Qwen-4B model optimized for mathematical reasoning, problem solving, and logical thinking tasks.

Trained with LoRA adapters

Exported in GGUF format (Q4_K_M) → efficient for local hosting

Compatible with Ollama, llama.cpp, and other GGUF-supported frameworks

Although trained only for next-token prediction, it demonstrates emergent reasoning capabilities, allowing it to handle complex tasks with surprising accuracy.

⚡ Features

Efficient 4-bit quantization (Q4_K_M): Low VRAM, ideal for personal/local inference

GGUF format: Works with Ollama, LM Studio, and other GGUF frameworks

Mathematics & logical reasoning: Solves problems, provides step-by-step explanations

Based on Qwen3 architecture: Strong generalization across tasks

Lightweight & fast: Optimized for inference speed without sacrificing accuracy

🏷️ Tags

text-generation-inference | transformers | unsloth | qwen3 | gguf

🎯 Intended Use

Mathematical problem solving

Logical reasoning tasks

Step-by-step explanations

Text generation in English

⚠️ Not recommended for: Critical decision-making or safety-critical applications (hallucinations possible)

💻 Usage Ollama (Local GGUF Hosting) ollama pull hf.co/NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF ollama run hf.co/NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF

Hugging Face Transformers (Python) from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "NisalDeZoysa/Qwen-4B-Mathematical-Thinking-GGUF"

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Solve the equation: 2x + 3 = 7" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚠️ Limitations

May produce incorrect or inconsistent outputs for very complex problems

4-bit quantization may lose some fidelity compared to full 16-bit models

Trained on public datasets, so may reflect inherent biases

📄 License

Released under the Apache-2.0 License

Downloads last month: 52

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

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