The Neumind-Math-7B-Instruct is a fine-tuned model based on Qwen2.5-7B-Instruct, optimized for mathematical reasoning, step-by-step problem-solving, and instruction-based tasks in the mathematics domain. The model is designed for applications requiring structured reasoning, numerical computations, and mathematical proof generation.
File Name
Size
Description
Upload Status
.gitattributes
1.81 kB
Git attributes configuration file
Uploaded
Neumind-Math-7B-Instruct.F16.gguf
15.2 GB
Model weights in FP16 precision
Uploaded (LFS)
Neumind-Math-7B-Instruct.Q4_K_M.gguf
4.68 GB
Quantized model (Q4)
Uploaded (LFS)
Neumind-Math-7B-Instruct.Q5_K_M.gguf
5.44 GB
Quantized model (Q5)
Uploaded (LFS)
Neumind-Math-7B-Instruct.Q8_0.gguf
8.1 GB
Quantized model (Q8)
Uploaded (LFS)
README.md
254 Bytes
Basic documentation for the model
Updated
config.json
31 Bytes
Minimal configuration for the model
Uploaded
Key Features:
Mathematical Reasoning:
Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.
Step-by-Step Problem Solving:
Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.
Instructional Applications:
Tailored for use in educational settings, such as tutoring systems, math content creation, and interactive learning tools.
Dataset: Trained on AI-MO/NuminaMath-CoT, a large dataset of mathematical problems and chain-of-thought (CoT) reasoning. The dataset contains 860k problems across various difficulty levels, enabling the model to tackle a wide spectrum of mathematical tasks.
Capabilities:
Complex Problem Solving:
Solves a wide range of mathematical problems, from basic arithmetic to advanced calculus and algebraic equations.
Chain-of-Thought Reasoning:
Excels in step-by-step logical reasoning, making it suitable for tasks requiring detailed explanations.
Instruction-Based Generation:
Ideal for generating educational content, such as worked examples, quizzes, and tutorials.
Usage Instructions:
Model Setup:
Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.
Inference:
Load the model using frameworks like PyTorch and Hugging Face Transformers. Ensure the pytorch_model.bin.index.json file is in the same directory for shard-based loading.
Customization:
Adjust generation parameters using generation_config.json to optimize outputs for your specific application.
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.
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:
plaintext
1>>> write a mini passage about space x
2Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
3With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
4the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
5demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
6efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
7increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
8sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
9plays a pivotal role in pushing the boundaries of human exploration and settlement.
Applications:
Education:
Interactive math tutoring, content creation, and step-by-step problem-solving tools.
Research:
Automated theorem proving and symbolic mathematics.
General Use:
Solving everyday mathematical queries and generating numerical datasets.
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.
