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Anish Agrawal

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replied to Kseniase's post 2 days ago

10 awesome advanced LoRA approaches Low-Rank Adaptation (LoRA) is the go-to method for efficient model fine-tuning that adds small low-rank matrices instead of retraining full models. The field isn’t standing still – new LoRA variants push the limits of efficiency, generalization, and personalization. So we’re sharing 10 of the latest LoRA approaches you should know about: 1. Mixture-of-LoRA-experts → https://huggingface.co/papers/2509.13878 Adds multiple low-rank adapters (LoRA) into a model’s layers, and a routing mechanism activates the most suitable ones for each input. This lets the model adapt better to new unseen conditions 2. Amortized Bayesian Meta-Learning for LoRA (ABMLL) → https://huggingface.co/papers/2508.14285 Balances global and task-specific parameters within a Bayesian framework to improve uncertainty calibration and generalization to new tasks without high memory or compute costs 3. AutoLoRA → https://huggingface.co/papers/2508.02107 Automatically retrieves and dynamically aggregates public LoRAs for stronger T2I generation 4. aLoRA (Activated LoRA) → https://huggingface.co/papers/2504.12397 Only applies LoRA after invocation, letting the model reuse the base model’s KV cache instead of recomputing the full turn’s KV cache. Efficient in multi-turn conversations 5. LiLoRA (LoRA in LoRA) → https://huggingface.co/papers/2508.06202 Shares the LoRA matrix A across tasks and additionally low-rank-decomposes matrix B to cut parameters in continual vision-text MLLMs 6. Sensitivity-LoRA → https://huggingface.co/papers/2509.09119 Dynamically assigns ranks to weight matrices based on their sensitivity, measured using second-order derivatives Read further below ↓ Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe

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