fangevo/KD-efficient-text-summarization
The project leverages a larger model, Qwen2.5-14B, to generate high-quality reference summaries, which are then used to fine-tune the SLM (Qwen2.5-0.5B), enabling the SLM to generate accurate news summaries. The LLM is optimized to run efficiently on free Google Colab (15GB VRAM), using 4-bit quantization and LoRA.
This project helps AI developers create a smaller, faster AI model that can summarize news articles accurately. It takes a large, high-quality AI model's summaries and uses them to teach a much smaller AI model. The outcome is an efficient summarization tool that can run on standard cloud computing resources, ideal for developers building AI-powered applications.
Use this if you are an AI developer who needs to create an efficient, lightweight text summarization model capable of summarizing news articles without requiring extensive computational resources.
Not ideal if you are an end-user simply looking to summarize text without any development or model training involvement.
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Language
Python
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Last pushed
Feb 11, 2026
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