prithivMLmods/SmolLM2-Math-IIO-1.7B-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.

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Training Details:

• Base Model: SmolLM2-1.7B
• 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.

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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.

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