ckryu84/gemma-3-1b-it-Math-SFT-Math-SFT

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Mar 25, 2026Architecture:Transformer Warm

The ckryu84/gemma-3-1b-it-Math-SFT-Math-SFT is a 1 billion parameter model based on the Gemma architecture. This model is fine-tuned for specific tasks, indicated by 'it' (instruction-tuned) and 'Math-SFT' (Math Supervised Fine-Tuning) in its name. With a context length of 32768 tokens, it is designed to excel in mathematical reasoning and problem-solving applications. Its primary strength lies in handling complex numerical and logical challenges.

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Model Overview

This model, ckryu84/gemma-3-1b-it-Math-SFT-Math-SFT, is a 1 billion parameter language model built upon the Gemma architecture. The naming convention suggests it has undergone instruction-tuning (-it) and supervised fine-tuning specifically for mathematical tasks (-Math-SFT). It supports a substantial context length of 32768 tokens, which is beneficial for processing longer mathematical problems or complex logical sequences.

Key Characteristics

  • Architecture: Gemma-based, indicating a robust foundation from Google's open models.
  • Parameter Count: 1 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: 32768 tokens, enabling the model to handle extensive inputs and maintain coherence over long mathematical or logical contexts.
  • Fine-tuning Focus: Explicitly fine-tuned for mathematical tasks, suggesting enhanced capabilities in numerical reasoning, problem-solving, and understanding mathematical concepts.

Potential Use Cases

  • Mathematical Problem Solving: Ideal for applications requiring the solution of arithmetic, algebra, geometry, or calculus problems.
  • Educational Tools: Can be integrated into platforms for tutoring, generating practice problems, or explaining mathematical concepts.
  • Data Analysis and Interpretation: Useful for tasks involving numerical data interpretation and generating insights from quantitative information.
  • Logical Reasoning: Its mathematical fine-tuning likely translates to improved performance in general logical reasoning tasks.