TensorGuard classifies fine-tuned LLMs into their base-model families with 94% accuracy by clustering statistical features of weight gradients under random input perturbations.
Fine-Tuning Qwen 2.5 3B for Realistic Movie Dialogue Generation
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abstract
The Qwen 2.5 3B base model was fine-tuned to generate contextually rich and engaging movie dialogue, leveraging the Cornell Movie-Dialog Corpus, a curated dataset of movie conversations. Due to the limitations in GPU computing and VRAM, the training process began with the 0.5B model progressively scaling up to the 1.5B and 3B versions as efficiency improvements were implemented. The Qwen 2.5 series, developed by Alibaba Group, stands at the forefront of small open-source pre-trained models, particularly excelling in creative tasks compared to alternatives like Meta's Llama 3.2 and Google's Gemma. Results demonstrate the ability of small models to produce high-quality, realistic dialogue, offering a promising approach for real-time, context-sensitive conversation generation.
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Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification
TensorGuard classifies fine-tuned LLMs into their base-model families with 94% accuracy by clustering statistical features of weight gradients under random input perturbations.