TensorGuard classifies fine-tuned LLMs into their base-model families with 94% accuracy by clustering statistical features of weight gradients under random input perturbations.
Extending Llama-3's Context Ten-Fold Overnight
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We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU machine. The resulted model exhibits superior performances across a broad range of evaluation tasks, such as NIHS, topic retrieval, and long-context language understanding; meanwhile, it also well preserves the original capability over short contexts. The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4 , which indicates the LLMs' inherent (yet largely underestimated) potential to extend its original context length. In fact, the context length could be extended far beyond 80K with more computation resources. Therefore, the team will publicly release the entire resources (including data, model, data generation pipeline, training code) so as to facilitate the future research from the community: \url{https://github.com/FlagOpen/FlagEmbedding}.
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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.