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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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- HuggingFaceTB/SmolLM2-1.7B-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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- pytorch |
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- llama |
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- trl |
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- ollama |
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- llama-cpp |
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- math |
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- instruct |
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--- |
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### SmolLM2-Math-IIO-1.7B-Instruct |
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The **SmolLM2-Math-IIO-1.7B-Instruct** model is a fine-tuned variant of the **SmolLM2-1.7B** architecture, optimized for mathematical instruction and reasoning tasks. It is particularly suited for applications that require mathematical problem-solving, logical inference, and detailed step-by-step explanations. |
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| File Name | Size | Description | Upload Status | |
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|----------------------------------------|------------|------------------------------------------------|----------------| |
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| `.gitattributes` | 1.52 kB | Git attributes configuration file | Uploaded | |
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| `README.md` | 287 Bytes | Updated README file | Updated | |
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| `config.json` | 940 Bytes | Model configuration settings | Uploaded | |
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| `generation_config.json` | 162 Bytes | Generation-specific configurations | Uploaded | |
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| `merges.txt` | 515 kB | Merging information for tokenization | Uploaded | |
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| `pytorch_model.bin` | 3.42 GB | Full model weights (PyTorch format) | Uploaded (LFS) | |
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| `special_tokens_map.json` | 572 Bytes | Mapping for special tokens used by the model | Uploaded | |
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| `tokenizer.json` | 3.77 MB | Tokenizer configuration and vocabulary | Uploaded | |
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| `tokenizer_config.json` | 3.95 kB | Tokenizer configuration for loading and usage | Uploaded | |
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| `vocab.json` | 801 kB | Vocabulary for the tokenizer | Uploaded | |
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### **Key Features:** |
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1. **Math-Focused Capabilities:** |
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This model is fine-tuned to handle a wide range of mathematical queries, from simple arithmetic to complex equations and mathematical proofs. |
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2. **Instruction-Tuned:** |
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Specifically trained to follow structured queries and deliver clear, coherent outputs based on instructions, ensuring high-quality, relevant responses to prompts. |
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3. **Tokenizer & Custom Tokens:** |
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Includes a robust tokenizer configuration with support for mathematical notation, custom tokens, and an extended vocabulary for accurate understanding and output generation. |
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### **Training Details:** |
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- **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) |
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- **Dataset:** Trained on **Math-IIO-68K-Mini**, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples. |
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### **Capabilities:** |
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- **Mathematical Problem-Solving:** Solves and explains complex mathematical problems, including algebra, calculus, and more advanced topics. |
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- **Instruction-Following:** Adheres to structured inputs and outputs, making it effective for generating step-by-step solutions. |
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- **Text Generation:** Capable of generating mathematical proofs, explanations, and educational content tailored to various user queries. |
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### **Usage Instructions:** |
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1. **Model Setup:** Download all model files and ensure the PyTorch model weights and tokenizer configurations are included. |
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2. **Inference:** Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers. |
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3. **Customization:** Configure the model with the `config.json` and `generation_config.json` files for optimal performance during inference. |
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