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https://www.amitkatyayana.com/

AI & ML interests

Training LLMs • Prompt Engineering • Fine-tuning • Retrieval-Augmented Generation (RAG) • LoRA • NLP • Educational AI • Financial NLP • Chatbot Design • Model Deployment • Transformers • Open-Source Contributions

Recent Activity

reacted to giadap's post with 👍 17 days ago
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive. In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward. ✨ Transparency can make safety mechanisms into learning opportunities. ✨ Collaboration with diverse communities makes safeguards more relevant across contexts. ✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules. Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy. Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
reacted to Kseniase's post with ❤️ 25 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
reacted to Kseniase's post with 👍 26 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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