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 thepytorch_model.bin.index.jsonfile is in the same directory for shard-based loading. -
Customization:
Adjust generation parameters usinggeneration_config.jsonto 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.