thangvip/Qwen3-1.7B-SFT-math-1500
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Jan 22, 2026Architecture:Transformer Warm
thangvip/Qwen3-1.7B-SFT-math-1500 is a 2 billion parameter language model, fine-tuned from Qwen/Qwen3-1.7B using Supervised Fine-Tuning (SFT) with the TRL framework. This model is specifically optimized for mathematical reasoning and problem-solving tasks, building upon the Qwen3 architecture. It is designed to enhance performance in quantitative domains, making it suitable for applications requiring strong mathematical capabilities.
Loading preview...
Model Overview
thangvip/Qwen3-1.7B-SFT-math-1500 is a 2 billion parameter language model derived from the Qwen/Qwen3-1.7B base model. It has undergone Supervised Fine-Tuning (SFT) using the TRL framework, indicating a focus on improving specific task performance through targeted training.
Key Capabilities
- Mathematical Reasoning: The model's SFT training suggests an optimization for mathematical tasks, aiming to enhance its ability to understand and solve quantitative problems.
- Qwen3 Architecture: Built upon the Qwen3-1.7B foundation, it inherits the general language understanding and generation capabilities of the Qwen family.
- TRL Framework: Training with TRL (Transformer Reinforcement Learning) implies a structured approach to fine-tuning, potentially leading to more robust and specialized performance in its intended domain.
Good For
- Mathematical Problem Solving: Ideal for applications requiring a language model with enhanced capabilities in arithmetic, algebra, and other mathematical reasoning tasks.
- Specialized Language Generation: Suitable for generating text or responses where mathematical accuracy and logical consistency are paramount.
- Research and Development: Can serve as a base for further experimentation and fine-tuning on specific mathematical datasets or applications.