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

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

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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:

  1. Mathematical Reasoning:
    Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.

  2. Step-by-Step Problem Solving:
    Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.

  3. 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:

  1. Model Setup:
    Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.

  2. 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.

  3. 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.

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Dataset used to train QuantFactory/Neumind-Math-7B-Instruct-GGUF