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---
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license: creativeml-openrail-m
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datasets:
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- AI-MO/NuminaMath-CoT
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- Qwen2.5
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- Ollama
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- Neumind
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- Math
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- Instruct
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- safetensors
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- pytorch
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- trl
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/Neumind-Math-7B-Instruct-GGUF
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This is quantized version of [prithivMLmods/Neumind-Math-7B-Instruct](https://huggingface.co/prithivMLmods/Neumind-Math-7B-Instruct) created using llama.cpp
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# Original Model Card
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### Neumind-Math-7B-Instruct Model Files
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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.
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| File Name | Size | Description | Upload Status |
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|------------------------------------|------------|------------------------------------------|----------------|
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| `.gitattributes` | 1.57 kB | Git attributes configuration file | Uploaded |
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| `README.md` | 265 Bytes | ReadMe file with basic information | Updated |
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| `added_tokens.json` | 657 Bytes | Additional token definitions | Uploaded |
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| `config.json` | 860 Bytes | Model configuration settings | Uploaded |
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| `generation_config.json` | 281 Bytes | Generation settings | Uploaded |
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| `merges.txt` | 1.82 MB | Tokenizer merge rules | Uploaded |
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| `pytorch_model-00001-of-00004.bin` | 4.88 GB | Model shard 1 of 4 | Uploaded (LFS) |
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| `pytorch_model-00002-of-00004.bin` | 4.93 GB | Model shard 2 of 4 | Uploaded (LFS) |
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| `pytorch_model-00003-of-00004.bin` | 4.33 GB | Model shard 3 of 4 | Uploaded (LFS) |
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| `pytorch_model-00004-of-00004.bin` | 1.09 GB | Model shard 4 of 4 | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 28.1 kB | Model index JSON | Uploaded |
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| `special_tokens_map.json` | 644 Bytes | Mapping of special tokens | Uploaded |
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| `tokenizer.json` | 11.4 MB | Tokenizer configuration | Uploaded (LFS) |
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| `tokenizer_config.json` | 7.73 kB | Additional tokenizer settings | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary for tokenization | Uploaded |
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---
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### **Key Features:**
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1. **Mathematical Reasoning:**
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Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.
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2. **Step-by-Step Problem Solving:**
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Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.
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3. **Instructional Applications:**
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Tailored for use in educational settings, such as tutoring systems, math content creation, and interactive learning tools.
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---
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### **Training Details:**
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- **Base Model:** [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
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- **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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---
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### **Capabilities:**
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- **Complex Problem Solving:**
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Solves a wide range of mathematical problems, from basic arithmetic to advanced calculus and algebraic equations.
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- **Chain-of-Thought Reasoning:**
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Excels in step-by-step logical reasoning, making it suitable for tasks requiring detailed explanations.
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- **Instruction-Based Generation:**
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Ideal for generating educational content, such as worked examples, quizzes, and tutorials.
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---
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### **Usage Instructions:**
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1. **Model Setup:**
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Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.
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2. **Inference:**
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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.
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3. **Customization:**
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Adjust generation parameters using `generation_config.json` to optimize outputs for your specific application.
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---
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### **Applications:**
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- **Education:**
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Interactive math tutoring, content creation, and step-by-step problem-solving tools.
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- **Research:**
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Automated theorem proving and symbolic mathematics.
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- **General Use:**
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Solving everyday mathematical queries and generating numerical datasets.
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---
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