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README.md

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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
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](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.
+---