Model Overview

This model, ali-elganzory/Qwen3-1.7B-Base-SFT-Tulu3-decontaminated, is an instruction-tuned variant of the Qwen3-1.7B-Base architecture. It was developed by ali-elganzory and fine-tuned using the TRL (Transformer Reinforcement Learning) library, specifically employing Supervised Fine-Tuning (SFT) techniques.

Key Characteristics

• Base Model: Qwen/Qwen3-1.7B-Base, a 1.7 billion parameter model.
• Fine-tuning Method: Supervised Fine-Tuning (SFT) using the TRL framework.
• Training Environment: The training process was tracked and visualized using Weights & Biases.
• Framework Versions: Utilizes TRL 0.27.0, Transformers 4.57.6, Pytorch 2.6.0+cu126, Datasets 4.5.0, and Tokenizers 0.22.2.

Intended Use Cases

This model is suitable for various text generation tasks where a compact, instruction-following language model is beneficial. Its fine-tuning aims to enhance its ability to respond to user prompts effectively, making it a good candidate for:

• General question answering.
• Conversational AI.
• Text completion based on instructions.
• Prototyping applications requiring a smaller, efficient LLM.