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

Libraries

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

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

pipe = pipeline("text-generation", model="prithivMLmods/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("prithivMLmods/Math-IIO-7B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/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="prithivMLmods/Math-IIO-7B-Instruct-GGUF",
	filename="Math-IIO-7B-Instruct.F16.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 prithivMLmods/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 prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/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 prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/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 prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/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 prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/Math-IIO-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/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 "prithivMLmods/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": "prithivMLmods/Math-IIO-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio new

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

Pi new

How to use prithivMLmods/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 prithivMLmods/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": "prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Math-IIO-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Math-IIO-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/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

Math IIO 7B Instruct GGUF

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.

File Name	Size	Description	Upload Status
`.gitattributes`	1.79 kB	Git attributes configuration file	Uploaded
`Math-IIO-7B-Instruct.F16.gguf`	15.2 GB	Full precision (FP16) model weights	Uploaded (LFS)
`Math-IIO-7B-Instruct.Q4_K_M.gguf`	4.68 GB	Quantized model weights (Q4_K_M)	Uploaded (LFS)
`Math-IIO-7B-Instruct.Q5_K_M.gguf`	5.44 GB	Quantized model weights (Q5_K_M)	Uploaded (LFS)
`Math-IIO-7B-Instruct.Q8_0.gguf`	8.1 GB	Quantized model weights (Q8_0)	Uploaded (LFS)
`Modelfile`	1.69 kB	Model metadata and structure definition	Uploaded
`README.md`	259 Bytes	Minimal README file	Updated
`config.json`	29 Bytes	Model configuration settings	Uploaded

Single Shot Answers

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.

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.

Run with Ollama [ Ollama Run ]

Overview

Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.

Download and Install Ollama
Steps to Run GGUF Models
Running the Model
Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Steps to Run GGUF Models

1. Create the Model File

First, create a model file and name it appropriately. For example, you can name your model file metallama.

2. Add the Template Command

In your model file, include a FROM line that specifies the base model file you want to use. For instance:

FROM Llama-3.2-1B.F16.gguf

Ensure that the model file is in the same directory as your script.

3. Create and Patch the Model

Open your terminal and run the following command to create and patch your model:

ollama create metallama -f ./metallama

Once the process is successful, you will see a confirmation message.

To verify that the model was created successfully, you can list all models with:

ollama list

Make sure that metallama appears in the list of models.

Running the Model

To run your newly created model, use the following command in your terminal:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

Conclusion

With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.

This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.

Downloads last month: 61

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

prithivMLmods/Math-IIO-7B-Instruct

Quantized

(3)

this model

Dataset used to train prithivMLmods/Math-IIO-7B-Instruct-GGUF

Collections including prithivMLmods/Math-IIO-7B-Instruct-GGUF

GPT-Generated Unified Format (GGUF)

Collection

ease of reading • 53 items • Updated 18 days ago • 4

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