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---
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license: creativeml-openrail-m
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datasets:
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- prithivMLmods/Math-IIO-68K-Mini
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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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- safetensors
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- qwen2.5
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- 7B
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- Instruct
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- Math
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- CoT
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- one-shot
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/Math-IIO-7B-Instruct-GGUF
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This is quantized version of [prithivMLmods/Math-IIO-7B-Instruct](https://huggingface.co/prithivMLmods/Math-IIO-7B-Instruct) created using llama.cpp
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# Original Model Card
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### **Math IIO 7B Instruct**
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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.
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### **Key Features:**
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1. **Math-Optimized Capabilities:**
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The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.
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2. **Instruction-Tuned:**
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Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.
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3. **Large Vocabulary:**
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Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.
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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` | 263 Bytes | README file with minimal details | Updated |
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| `added_tokens.json` | 657 Bytes | Custom added tokens for tokenizer | Uploaded |
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| `config.json` | 861 Bytes | Model configuration file | Uploaded |
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| `generation_config.json` | 281 Bytes | Configuration for text generation settings | Uploaded |
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| `merges.txt` | 1.82 MB | Merge rules for byte pair encoding tokenizer | Uploaded |
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| `pytorch_model-00001-of-00004.bin` | 4.88 GB | First part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00002-of-00004.bin` | 4.93 GB | Second part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00003-of-00004.bin` | 4.33 GB | Third part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model-00004-of-00004.bin` | 1.09 GB | Fourth part of model weights (PyTorch) | Uploaded (LFS) |
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| `pytorch_model.bin.index.json` | 28.1 kB | Index JSON file for model weights | Uploaded |
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| `special_tokens_map.json` | 644 Bytes | Map of special tokens used by the tokenizer | Uploaded |
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| `tokenizer.json` | 11.4 MB | Tokenizer settings and vocab | Uploaded (LFS) |
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| `tokenizer_config.json` | 7.73 kB | Configuration for tokenizer | Uploaded |
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| `vocab.json` | 2.78 MB | Vocabulary for tokenizer | Uploaded |
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### **Training Details:**
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- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](#)
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- **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.
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### **Capabilities:**
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- **Problem-Solving:** Solves mathematical problems ranging from basic arithmetic to advanced calculus and linear algebra.
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- **Educational Use:** Explains solutions step-by-step, making it a valuable teaching assistant.
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- **Analysis & Reasoning:** Handles logical reasoning tasks and computational queries effectively.
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### **How to Use:**
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1. Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.
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2. Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.
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3. Use the provided configurations (`config.json` and `generation_config.json`) for optimal inference.
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