Pradipta Deb

pradiptadeb90

AI & ML interests

LLM, NLP & Privacy Research

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reacted to fdaudens's post with ❤️ 10 days ago

Just completed the AI Agents course and wow, that capstone project really makes you understand how to build agents that can handle real-world complexity! The final project uses the GAIA dataset - your agent has to solve tasks like analyzing Excel files, processing audio recordings, answering questions about YouTube videos, and diving into research papers. This isn't toy examples, it's the messy, multimodal stuff agents need to handle in practice. Whether you’re just getting started with agents or want to go deeper with tools like LangChain, LlamaIndex, and SmolAgents, this course has tons of useful stuff. A few key insights: - Code agents are incredibly versatile once you get the architecture right - The sweet spot is finding the right balance of guidance vs autonomy for each use case - Once the logic clicks, the possibilities really are endless - it's like letting LLMs break free from the chatbox The course is free and the certification deadline is July 1st, 2025. The Hugging Face team built something special here. If you're tired of AI that impresses in demos but fails in practice, this is your path to building agents that actually deliver. https://huggingface.co/learn/agents-course/unit0/introduction Best part? There's the MCP course next!

liked a model 3 months ago

ds4sd/SmolDocling-256M-preview

reacted to Kseniase's post with 👍 3 months ago

15 types of attention mechanisms Attention mechanisms allow models to dynamically focus on specific parts of their input when performing tasks. In our recent article, we discussed Multi-Head Latent Attention (MLA) in detail and now it's time to summarize other existing types of attention. Here is a list of 15 types of attention mechanisms used in AI models: 1. Soft attention (Deterministic attention) -> https://huggingface.co/papers/1409.0473 Assigns a continuous weight distribution over all parts of the input. It produces a weighted sum of the input using attention weights that sum to 1. 2. Hard attention (Stochastic attention) -> https://huggingface.co/papers/1508.04025 Makes a discrete selection of some part of the input to focus on at each step, rather than attending to everything. 3. Self-attention -> https://huggingface.co/papers/1706.03762 Each element in the sequence "looks" at other elements and "decides" how much to borrow from each of them for its new representation. 4. Cross-Attention (Encoder-Decoder attention) -> https://huggingface.co/papers/2104.08771 The queries come from one sequence and the keys/values come from another sequence. It allows a model to combine information from two different sources. 5. Multi-Head Attention (MHA) -> https://huggingface.co/papers/1706.03762 Multiple attention “heads” are run in parallel. The model computes several attention distributions (heads), each with its own set of learned projections of queries, keys, and values. 6. Multi-Head Latent Attention (MLA) -> https://huggingface.co/papers/2405.04434 Extends MHA by incorporating a latent space where attention heads can dynamically learn different latent factors or representations. 7. Memory-Based attention -> https://huggingface.co/papers/1503.08895 Involves an external memory and uses attention to read from and write to this memory. See other types in the comments 👇

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