Instructions to use Habibur2/Qwen2.5-Math-1.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Habibur2/Qwen2.5-Math-1.5B-GGUF",
	filename="qwen-math-1.5b-q4_k_m.gguf",
)

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

llama-cpp-python

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Habibur2/Qwen2.5-Math-1.5B-GGUF",
	filename="qwen-math-1.5b-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Habibur2/Qwen2.5-Math-1.5B-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 Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Habibur2/Qwen2.5-Math-1.5B-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 Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Habibur2/Qwen2.5-Math-1.5B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Habibur2/Qwen2.5-Math-1.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

Ollama
How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with Ollama:
```
ollama run hf.co/Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Habibur2/Qwen2.5-Math-1.5B-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 Habibur2/Qwen2.5-Math-1.5B-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 Habibur2/Qwen2.5-Math-1.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Habibur2/Qwen2.5-Math-1.5B-GGUF to start chatting

Pi new

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Habibur2/Qwen2.5-Math-1.5B-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": "Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Habibur2/Qwen2.5-Math-1.5B-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 Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M
```

Lemonade

How to use Habibur2/Qwen2.5-Math-1.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Habibur2/Qwen2.5-Math-1.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Math-1.5B-GGUF-Q4_K_M

List all available models

lemonade list

🧮 Qwen 2.5 Math 1.5B (GGUF Quantized)

This repository contains the GGUF quantized version of the Qwen 2.5 Math 1.5B model.

It is a specialized Mathematical Reasoning Model optimized for edge devices, offline usage, and educational apps. Despite its small size (1.5B), it outperforms many larger general-purpose models in complex mathematical problem-solving tasks.

Quantized By: Md Habibur Rahman (Aasif)
Quantization Format: GGUF (Q4_K_M) - Optimized for balance between Math Accuracy and Speed.

🌟 Key Features

Math Specialist: Specifically trained on massive mathematical datasets (Algebra, Calculus, Geometry, Logic).
Chain-of-Thought (CoT): Capable of showing step-by-step reasoning for solving problems.
Edge AI Ready: Extremely lightweight (~1 GB). Runs smoothly on Android, Raspberry Pi, and Older Laptops.
Offline Capable: Does not require an internet connection to solve problems.

🚀 Usage (Python)

You can run this model using the llama-cpp-python library.

1. Installation

pip install llama-cpp-python huggingface_hub

Python Inference Code

Here is a script to solve math problems with step-by-step logic:

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# Download the model
model_path = hf_hub_download(
    repo_id="Habibur2/Qwen2.5-Math-1.5B-GGUF",
    filename="qwen-math-1.5b-q4_k_m.gguf"
)

# Load Model
# Set n_gpu_layers=-1 for full GPU usage (Fastest)
# Set n_gpu_layers=0 for CPU only
llm = Llama(
    model_path=model_path,
    n_ctx=2048,        # Context Window
    n_threads=4,       # CPU Threads
    n_gpu_layers=-1    # GPU Acceleration
)

# Define a Math Problem
math_problem = "Find the integral of x^2 + 5x with respect to x."

# System Prompt is Crucial for Math Models
messages = [
    {"role": "system", "content": "You are a helpful mathematical assistant. Please solve the problem step-by-step and show your reasoning clearly."},
    {"role": "user", "content": math_problem}
]

# Generate Solution
output = llm.create_chat_completion(
    messages=messages,
    max_tokens=1024,   # Math solutions need more tokens
    temperature=0.1    # Low temperature (0.1) is best for precise math
)

print("🤖 Solution:\n")
print(output['choices'][0]['message']['content'])

⚙️ Technical Specifications

Feature,Details Original Model,Qwen 2.5 Math 1.5B Instruct Architecture,"Transformer (RoPE, SwiGLU)" Parameters,1.5 Billion Quantization Type,Q4_K_M (4-bit Medium) File Size,~1.12 GB Recommended RAM,2 GB+

🧪 Benchmark & Capabilities

This model excels at:

Algebra & Arithmetic: Solving equations, inequalities, and basic operations.

Calculus: Differentiation and Integration problems.

Word Problems: Understanding and translating text into mathematical equations.

LaTeX Output: Can generate answers in LaTeX format for academic rendering.

👨‍💻 About the Project

This model was quantized and uploaded by Md Habibur Rahman as part of a research initiative on Offline Edge AI & Small Language Models (SLM). The goal is to democratize access to powerful educational AI tools without relying on heavy cloud infrastructure.

Disclaimer: While this model is highly capable, always verify complex mathematical solutions.

Downloads last month: 20

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Habibur2/Qwen2.5-Math-1.5B-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Math-1.5B

Quantized

(14)

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