| |
|
| | --- |
| | |
| | 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 |
| |
|
| | --- |
| | |
| | [](https://hf.co/QuantFactory) |
| |
|
| |
|
| | # QuantFactory/Math-IIO-7B-Instruct-GGUF |
| | This is quantized version of [prithivMLmods/Math-IIO-7B-Instruct](https://huggingface.co/prithivMLmods/Math-IIO-7B-Instruct) created using llama.cpp |
| |
|
| | # Original Model Card |
| |
|
| |  |
| |
|
| | ### **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. |
| |
|
| | | File Name | 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 | |
| |
|
| | ### **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. |
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