---
license: creativeml-openrail-m
datasets:
- prithivMLmods/Math-IIO-68K-Mini
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- qwen2.5
- 7B
- Instruct
- Math
- CoT
- one-shot
---
![aaa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/faLfR-doaWP_BLUkOQrbq.png)

### **Math IIO 7B Instruct**  

The **Math IIO 7B Instruct** is a fine-tuned language model based on the robust **Qwen2.5-7B-Instruct** architecture. This model has been specifically trained to excel in single-shot mathematical reasoning and instruction-based tasks, making it a reliable choice for educational, analytical, and problem-solving applications.

### **Key Features:**

1. **Math-Optimized Capabilities:**  
   The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.  

2. **Instruction-Tuned:**  
   Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.

3. **Large Vocabulary:**  
   Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.

### Single Shot Answers

![solution.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4Zq6crBrbFLDqfKlDwBMU.png)

### Math-IIO File Structure

| File Name [ Uploaded file ]                         | Size       | Description                                   | Upload Status  |
|------------------------------------|------------|-----------------------------------------------|----------------|
| `.gitattributes`                   | 1.57 kB    | Git attributes configuration file             | Uploaded       |
| `README.md`                        | 263 Bytes  | README file with minimal details              | Updated        |
| `added_tokens.json`                | 657 Bytes  | Custom added tokens for tokenizer             | Uploaded       |
| `config.json`                      | 861 Bytes  | Model configuration file                      | Uploaded       |
| `generation_config.json`           | 281 Bytes  | Configuration for text generation settings    | Uploaded       |
| `merges.txt`                       | 1.82 MB    | Merge rules for byte pair encoding tokenizer  | Uploaded       |
| `pytorch_model-00001-of-00004.bin` | 4.88 GB    | First part of model weights (PyTorch)         | Uploaded (LFS) |
| `pytorch_model-00002-of-00004.bin` | 4.93 GB    | Second part of model weights (PyTorch)        | Uploaded (LFS) |
| `pytorch_model-00003-of-00004.bin` | 4.33 GB    | Third part of model weights (PyTorch)         | Uploaded (LFS) |
| `pytorch_model-00004-of-00004.bin` | 1.09 GB    | Fourth part of model weights (PyTorch)        | Uploaded (LFS) |
| `pytorch_model.bin.index.json`     | 28.1 kB    | Index JSON file for model weights             | Uploaded       |
| `special_tokens_map.json`          | 644 Bytes  | Map of special tokens used by the tokenizer   | Uploaded       |
| `tokenizer.json`                   | 11.4 MB    | Tokenizer settings and vocab                  | Uploaded (LFS) |
| `tokenizer_config.json`            | 7.73 kB    | Configuration for tokenizer                   | Uploaded       |
| `vocab.json`                       | 2.78 MB    | Vocabulary for tokenizer                      | Uploaded       |

| Model Type | Size | Context Length | Link |
|------------|------|----------------|------|
| GGUF | 7B | - | [🤗 Math-IIO-7B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Math-IIO-7B-Instruct-GGUF) |

### **Training Details:**
- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](#)  
- **Dataset:** Trained on **Math-IIO-68K-Mini**, a curated dataset with 68.8k high-quality examples focusing on mathematical instructions, equations, and logic-based queries.

### **Capabilities:**
- **Problem-Solving:** Solves mathematical problems ranging from basic arithmetic to advanced calculus and linear algebra.  
- **Educational Use:** Explains solutions step-by-step, making it a valuable teaching assistant.  
- **Analysis & Reasoning:** Handles logical reasoning tasks and computational queries effectively.

### **How to Use:**
1. Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.  
2. Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.  
3. Use the provided configurations (`config.json` and `generation_config.json`) for optimal inference.

---