| | ---
|
| | license: creativeml-openrail-m
|
| | datasets:
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| | - prithivMLmods/Math-IIO-68K-Mini
|
| | language:
|
| | - zho
|
| | - eng
|
| | - fra
|
| | - spa
|
| | - por
|
| | - deu
|
| | - ita
|
| | - rus
|
| | - jpn
|
| | - kor
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| | - vie
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| | - tha
|
| | - ara
|
| | base_model:
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| | - Qwen/Qwen2.5-7B-Instruct
|
| | pipeline_tag: text-generation
|
| | library_name: transformers
|
| | tags:
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| | - safetensors
|
| | - qwen2.5
|
| | - 7B
|
| | - Instruct
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| | - Math
|
| | - CoT
|
| | - one-shot
|
| | ---
|
| | 
|
| |
|
| | ### **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:**
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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.
|
| |
|
| | 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.
|
| |
|
| | ### Single Shot Answers
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| |
|
| | 
|
| |
|
| | ### Math-IIO File Structure
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| |
|
| | | 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:**
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| | - **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:**
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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.
|
| |
|
| | ---
|
| |
|
| |
|
