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

Name: Neumind-Math-7B-Instruct-GGUF
Rating: 2.5 (414 reviews)

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

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

Original Model Card

Neumind-Math-7B-Instruct Model Files

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`	1.57 kB	Git attributes configuration file	Uploaded
`README.md`	265 Bytes	ReadMe file with basic information	Updated
`added_tokens.json`	657 Bytes	Additional token definitions	Uploaded
`config.json`	860 Bytes	Model configuration settings	Uploaded
`generation_config.json`	281 Bytes	Generation settings	Uploaded
`merges.txt`	1.82 MB	Tokenizer merge rules	Uploaded
`pytorch_model-00001-of-00004.bin`	4.88 GB	Model shard 1 of 4	Uploaded (LFS)
`pytorch_model-00002-of-00004.bin`	4.93 GB	Model shard 2 of 4	Uploaded (LFS)
`pytorch_model-00003-of-00004.bin`	4.33 GB	Model shard 3 of 4	Uploaded (LFS)
`pytorch_model-00004-of-00004.bin`	1.09 GB	Model shard 4 of 4	Uploaded (LFS)
`pytorch_model.bin.index.json`	28.1 kB	Model index JSON	Uploaded
`special_tokens_map.json`	644 Bytes	Mapping of special tokens	Uploaded
`tokenizer.json`	11.4 MB	Tokenizer configuration	Uploaded (LFS)
`tokenizer_config.json`	7.73 kB	Additional tokenizer settings	Uploaded
`vocab.json`	2.78 MB	Vocabulary for tokenization	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.

Training Details:

Base Model: Qwen2.5-7B
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.

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.