ali-elganzory/Qwen3-1.7B-Base-SFT-Tulu3-decontaminated
The ali-elganzory/Qwen3-1.7B-Base-SFT-Tulu3-decontaminated model is a fine-tuned version of the Qwen3-1.7B-Base architecture, developed by ali-elganzory. This 1.7 billion parameter model has been instruction fine-tuned using the TRL framework with Supervised Fine-Tuning (SFT). It is designed for general text generation tasks, leveraging its base Qwen3 capabilities enhanced by instruction following. This model is suitable for applications requiring a compact yet capable language model for conversational or prompt-based interactions.
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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.