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---
license: creativeml-openrail-m
datasets:
- AI-MO/NuminaMath-CoT
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Qwen2.5
- Ollama
- Neumind
- Math
- Instruct
- safetensors
- pytorch
- trl
---
[](https://hf.co/QuantFactory)
# QuantFactory/Neumind-Math-7B-Instruct-GGUF
This is quantized version of [prithivMLmods/Neumind-Math-7B-Instruct](https://huggingface.co/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:**
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](https://huggingface.co/Qwen/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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