Instructions to use QuantFactory/Math-IIO-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Math-IIO-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Math-IIO-7B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Math-IIO-7B-Instruct-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": "QuantFactory/Math-IIO-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Math-IIO-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Math-IIO-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Math-IIO-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Math-IIO-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Math-IIO-7B-Instruct-GGUF to start chatting

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Math-IIO-7B-Instruct-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": "QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Math-IIO-7B-Instruct-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 "QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Math-IIO-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-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 QuantFactory/Math-IIO-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Math-IIO-7B-Instruct-GGUF

File size: 4,643 Bytes

9c9a3c0


---

license: creativeml-openrail-m
datasets:
- prithivMLmods/Math-IIO-68K-Mini
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- qwen2.5
- 7B
- Instruct
- Math
- CoT
- one-shot

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/Math-IIO-7B-Instruct-GGUF
This is quantized version of [prithivMLmods/Math-IIO-7B-Instruct](https://huggingface.co/prithivMLmods/Math-IIO-7B-Instruct) created using llama.cpp

# Original Model Card

![aaa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/faLfR-doaWP_BLUkOQrbq.png)

### **Math IIO 7B Instruct**  

The **Math IIO 7B Instruct** is a fine-tuned language model based on the robust **Qwen2.5-7B-Instruct** architecture. This model has been specifically trained to excel in single-shot mathematical reasoning and instruction-based tasks, making it a reliable choice for educational, analytical, and problem-solving applications.

### **Key Features:**

1. **Math-Optimized Capabilities:**  
   The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.  

2. **Instruction-Tuned:**  
   Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.

3. **Large Vocabulary:**  
   Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.

| File Name                          | Size       | Description                                   | Upload Status  |
|------------------------------------|------------|-----------------------------------------------|----------------|
| `.gitattributes`                   | 1.57 kB    | Git attributes configuration file             | Uploaded       |
| `README.md`                        | 263 Bytes  | README file with minimal details              | Updated        |
| `added_tokens.json`                | 657 Bytes  | Custom added tokens for tokenizer             | Uploaded       |
| `config.json`                      | 861 Bytes  | Model configuration file                      | Uploaded       |
| `generation_config.json`           | 281 Bytes  | Configuration for text generation settings    | Uploaded       |
| `merges.txt`                       | 1.82 MB    | Merge rules for byte pair encoding tokenizer  | Uploaded       |
| `pytorch_model-00001-of-00004.bin` | 4.88 GB    | First part of model weights (PyTorch)         | Uploaded (LFS) |
| `pytorch_model-00002-of-00004.bin` | 4.93 GB    | Second part of model weights (PyTorch)        | Uploaded (LFS) |
| `pytorch_model-00003-of-00004.bin` | 4.33 GB    | Third part of model weights (PyTorch)         | Uploaded (LFS) |
| `pytorch_model-00004-of-00004.bin` | 1.09 GB    | Fourth part of model weights (PyTorch)        | Uploaded (LFS) |
| `pytorch_model.bin.index.json`     | 28.1 kB    | Index JSON file for model weights             | Uploaded       |
| `special_tokens_map.json`          | 644 Bytes  | Map of special tokens used by the tokenizer   | Uploaded       |
| `tokenizer.json`                   | 11.4 MB    | Tokenizer settings and vocab                  | Uploaded (LFS) |
| `tokenizer_config.json`            | 7.73 kB    | Configuration for tokenizer                   | Uploaded       |
| `vocab.json`                       | 2.78 MB    | Vocabulary for tokenizer                      | Uploaded       |

### **Training Details:**
- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](#)  
- **Dataset:** Trained on **Math-IIO-68K-Mini**, a curated dataset with 68.8k high-quality examples focusing on mathematical instructions, equations, and logic-based queries.

### **Capabilities:**
- **Problem-Solving:** Solves mathematical problems ranging from basic arithmetic to advanced calculus and linear algebra.  
- **Educational Use:** Explains solutions step-by-step, making it a valuable teaching assistant.  
- **Analysis & Reasoning:** Handles logical reasoning tasks and computational queries effectively.

### **How to Use:**
1. Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.  
2. Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.  
3. Use the provided configurations (`config.json` and `generation_config.json`) for optimal inference.