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Piyush Maharana

catastropiyush
thomwolf's profile picture eugrug-60's profile picture Mi6paulino's profile picture
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https://catastropiyush.github.io/

AI & ML interests

LLMs for scientific data extraction, Solid State Hydrogen Storage,Machine Learning

Recent Activity

reacted to burtenshaw's post with 🔥 3 days ago
We’re launching a FREE and CERTIFIED course on Agents! We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents. Here's what you'll learn: - Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions. - Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors. - Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents. - Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents. Audience This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents. Enroll today and start building the next generation of AI agent applications! https://bit.ly/hf-learn-agents
reacted to tomaarsen's post with 🔥 3 days ago
🏎️ Today I'm introducing a method to train static embedding models that run 100x to 400x faster on CPU than common embedding models, while retaining 85%+ of the quality! Including 2 fully open models: training scripts, datasets, metrics. We apply our recipe to train 2 Static Embedding models that we release today! We release: 2️⃣ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0 🧠 my modern training strategy: ideation -> dataset choice -> implementation -> evaluation 📜 my training scripts, using the Sentence Transformers library 📊 my Weights & Biases reports with losses & metrics 📕 my list of 30 training and 13 evaluation datasets The 2 Static Embedding models have the following properties: 🏎️ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5' 0️⃣ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed! 📏 No maximum sequence length! Embed texts at any length (note: longer texts may embed worse) 📐 Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more. 🪆 Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks) Check out the full blogpost if you'd like to 1) use these lightning-fast models or 2) learn how to train them with consumer-level hardware: https://huggingface.co/blog/static-embeddings The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance. Alternatively, check out the models: * https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1 * https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1
reacted to tomaarsen's post with ❤️ 3 days ago
🏎️ Today I'm introducing a method to train static embedding models that run 100x to 400x faster on CPU than common embedding models, while retaining 85%+ of the quality! Including 2 fully open models: training scripts, datasets, metrics. We apply our recipe to train 2 Static Embedding models that we release today! We release: 2️⃣ an English Retrieval model and a general-purpose Multilingual similarity model (e.g. classification, clustering, etc.), both Apache 2.0 🧠 my modern training strategy: ideation -> dataset choice -> implementation -> evaluation 📜 my training scripts, using the Sentence Transformers library 📊 my Weights & Biases reports with losses & metrics 📕 my list of 30 training and 13 evaluation datasets The 2 Static Embedding models have the following properties: 🏎️ Extremely fast, e.g. 107500 sentences per second on a consumer CPU, compared to 270 for 'all-mpnet-base-v2' and 56 for 'gte-large-en-v1.5' 0️⃣ Zero active parameters: No Transformer blocks, no attention, not even a matrix multiplication. Super speed! 📏 No maximum sequence length! Embed texts at any length (note: longer texts may embed worse) 📐 Linear instead of exponential complexity: 2x longer text takes 2x longer, instead of 2.5x or more. 🪆 Matryoshka support: allow you to truncate embeddings with minimal performance loss (e.g. 4x smaller with a 0.56% perf. decrease for English Similarity tasks) Check out the full blogpost if you'd like to 1) use these lightning-fast models or 2) learn how to train them with consumer-level hardware: https://huggingface.co/blog/static-embeddings The blogpost contains a lengthy list of possible advancements; I'm very confident that our 2 models are only the tip of the iceberg, and we may be able to get even better performance. Alternatively, check out the models: * https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1 * https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1
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