zlyngkhoi/qwen2.5-0.5b-instruction-following-sft
The zlyngkhoi/qwen2.5-0.5b-instruction-following-sft is a 0.5 billion parameter causal language model, fine-tuned by zlyngkhoi for instruction following. Built using Aligntune and based on the Qwen2.5-0.5B-Instruct architecture, this model is optimized for processing and responding to instructions. It leverages SFT (Supervised Fine-Tuning) with Unsloth and TRL backends, making it suitable for applications requiring efficient instruction-based text generation.
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Model Overview
This model, zlyngkhoi/qwen2.5-0.5b-instruction-following-sft, is a 0.5 billion parameter instruction-following language model. It was developed by zlyngkhoi and fine-tuned from the Qwen/Qwen2.5-0.5B-Instruct base model. The fine-tuning process utilized Aligntune, a framework supporting various open-source models and algorithms, specifically employing Supervised Fine-Tuning (SFT) for instruction following.
Key Characteristics
- Base Model: Qwen2.5-0.5B-Instruct
- Parameter Count: 0.5 billion
- Context Length: 32768 tokens
- Fine-tuning Method: SFT (Instruction Following)
- Backend Technologies: Unsloth + TRL, indicating an optimization for efficient training and inference.
Intended Use Cases
This model is designed for tasks that require adherence to specific instructions, making it suitable for:
- Generating text based on explicit prompts.
- Developing lightweight conversational agents.
- Applications where efficient instruction processing on a smaller model is beneficial.