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", dtype="auto")

llama-cpp-python

How to use QuantFactory/Math-IIO-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Math-IIO-7B-Instruct-GGUF",
	filename="Math-IIO-7B-Instruct.Q2_K.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 QuantFactory/Math-IIO-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

Pi new

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-server -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

Hermes Agent new

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-server -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

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

QuantFactory/Math-IIO-7B-Instruct-GGUF

This is quantized version of prithivMLmods/Math-IIO-7B-Instruct created using llama.cpp

Original Model Card

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:

Math-Optimized Capabilities:
The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.
Instruction-Tuned:
Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.
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:

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

Downloads last month: 320

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Math-IIO-7B-Instruct-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(310)

this model

QuantFactory
/

Math-IIO-7B-Instruct-GGUF