---
license: creativeml-openrail-m
datasets:
- prithivMLmods/Math-IIO-68K-Mini
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-1.7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- pytorch
- llama
- trl
- ollama
- llama-cpp
- math
- instruct
---

### SmolLM2-Math-IIO-1.7B-Instruct

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.

| File Name                              | Size       | Description                                    | Upload Status  |
|----------------------------------------|------------|------------------------------------------------|----------------|
| `.gitattributes`                       | 1.52 kB    | Git attributes configuration file              | Uploaded       |
| `README.md`                            | 287 Bytes  | Updated README file                            | Updated        |
| `config.json`                          | 940 Bytes  | Model configuration settings                   | Uploaded       |
| `generation_config.json`               | 162 Bytes  | Generation-specific configurations             | Uploaded       |
| `merges.txt`                           | 515 kB     | Merging information for tokenization           | Uploaded       |
| `pytorch_model.bin`                    | 3.42 GB    | Full model weights (PyTorch format)            | Uploaded (LFS) |
| `special_tokens_map.json`              | 572 Bytes  | Mapping for special tokens used by the model   | Uploaded       |
| `tokenizer.json`                       | 3.77 MB    | Tokenizer configuration and vocabulary         | Uploaded       |
| `tokenizer_config.json`                | 3.95 kB   | Tokenizer configuration for loading and usage  | Uploaded       |
| `vocab.json`                           | 801 kB     | Vocabulary for the tokenizer                   | Uploaded       |

### **Key Features:**

1. **Math-Focused Capabilities:**  
   This model is fine-tuned to handle a wide range of mathematical queries, from simple arithmetic to complex equations and mathematical proofs.  

2. **Instruction-Tuned:**  
   Specifically trained to follow structured queries and deliver clear, coherent outputs based on instructions, ensuring high-quality, relevant responses to prompts.

3. **Tokenizer & Custom Tokens:**  
   Includes a robust tokenizer configuration with support for mathematical notation, custom tokens, and an extended vocabulary for accurate understanding and output generation.

---

### **Training Details:**
- **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)  
- **Dataset:** Trained on **Math-IIO-68K-Mini**, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples.

### **Capabilities:**
- **Mathematical Problem-Solving:** Solves and explains complex mathematical problems, including algebra, calculus, and more advanced topics.  
- **Instruction-Following:** Adheres to structured inputs and outputs, making it effective for generating step-by-step solutions.  
- **Text Generation:** Capable of generating mathematical proofs, explanations, and educational content tailored to various user queries.

---

### **Usage Instructions:**
1. **Model Setup:** Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.  
2. **Inference:** Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.  
3. **Customization:** Configure the model with the `config.json` and `generation_config.json` files for optimal performance during inference.

---