vijay-ravichander/Qwen2.5-0.5B-Lexo-Sort-SFT-v1
vijay-ravichander/Qwen2.5-0.5B-Lexo-Sort-SFT-v1 is a 0.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct using Supervised Fine-Tuning (SFT) with the TRL framework. This model is designed for general text generation tasks, leveraging its Qwen2.5 base architecture and a 32768 token context length. Its fine-tuning process aims to enhance its conversational and instruction-following capabilities for various applications.
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Model Overview
vijay-ravichander/Qwen2.5-0.5B-Lexo-Sort-SFT-v1 is a 0.5 billion parameter language model developed by vijay-ravichander. It is a fine-tuned variant of the Qwen/Qwen2.5-0.5B-Instruct base model, utilizing Supervised Fine-Tuning (SFT) techniques implemented with the TRL framework.
Key Characteristics
- Base Model: Built upon the Qwen2.5-0.5B-Instruct architecture.
- Parameter Count: Features 0.5 billion parameters, making it a compact yet capable model.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Method: Fine-tuned using Supervised Fine-Tuning (SFT) to adapt its responses and improve instruction following.
- Frameworks: Training was conducted using TRL, Transformers, PyTorch, Datasets, and Tokenizers, with specific versions detailed in the original training procedure.
Use Cases
This model is suitable for a range of text generation tasks where a smaller, efficient model with good instruction-following capabilities is desired. Its fine-tuning aims to provide enhanced performance for conversational AI and general question-answering scenarios.