prithivMLmods/Neumind-Math-7B-Instruct

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

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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.

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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.

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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.

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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.

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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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