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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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- en |
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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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![aaa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/faLfR-doaWP_BLUkOQrbq.png) |
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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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### Single Shot Answers |
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![solution.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4Zq6crBrbFLDqfKlDwBMU.png) |
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### Math-IIO File Structure |
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| File Name [ Uploaded file ] | 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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| Model Type | Size | Context Length | Link | |
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|------------|------|----------------|------| |
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| GGUF | 7B | - | [🤗 Math-IIO-7B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Math-IIO-7B-Instruct-GGUF) | |
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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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