---

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

---

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# 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.
---