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lc2004

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reacted to ZennyKenny's post with 🔥 29 days ago

I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.

liked a model 30 days ago

BAAI/bge-m3

replied to schuler's post 30 days ago

📢 New Research Alert: Making Language Models Smaller & Smarter! Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance. The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena. 🔑 Key Findings: • 77% parameter reduction. • Maintained model capabilities. • Improved generalization. Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT Code: https://github.com/joaopauloschuler/less-parameters-llm

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reacted to ZennyKenny's post with 🔥 29 days ago

Post

3432

liked a model 30 days ago

BAAI/bge-m3

replied to schuler's post 30 days ago

amazing

upvoted a paper about 1 month ago

DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

Paper • 2502.01142 • Published Feb 3 • 24

reacted to Kseniase's post with 🔥 about 1 month ago

Post

7785

8 New Types of RAG

RAG techniques continuously evolve to enhance LLM response accuracy by retrieving relevant external data during generation. To keep up with current AI trends, new RAG types incorporate deep step-by-step reasoning, tree search, citations, multimodality and other effective techniques.

Here's a list of 8 latest RAG advancements:

1. DeepRAG -> DeepRAG: Thinking to Retrieval Step by Step for Large Language Models (2502.01142)
Models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic retrieval. It dynamically decides when to retrieve external knowledge and when rely on parametric reasoning.

2. RealRAG -> RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning (2502.00848)
Enhances novel object generation by retrieving real-world images and using self-reflective contrastive learning to fill knowledge gap, improve realism and reduce distortions.

3. Chain-of-Retrieval Augmented Generation (CoRAG) -> Chain-of-Retrieval Augmented Generation (2501.14342)
Retrieves information step-by-step and adjusts it, also deciding how much compute power to use at test time. If needed it reformulates queries.

4. VideoRAG -> VideoRAG: Retrieval-Augmented Generation over Video Corpus (2501.05874)
Enables unlimited-length video processing, using dual-channel architecture that integrates graph-based textual grounding and multi-modal context encoding.

5. CFT-RAG -> CFT-RAG: An Entity Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Filter (2501.15098)
A tree-RAG acceleration method uses an improved Cuckoo Filter to optimize entity localization, enabling faster retrieval.

6. Contextualized Graph RAG (CG-RAG) -> CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs (2501.15067)
Uses Lexical-Semantic Graph Retrieval (LeSeGR) to integrate sparse and dense signals within graph structure and capture citation relationships

7. GFM-RAG -> GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation (2502.01113)
A graph foundation model that uses a graph neural network to refine query-knowledge connections

8. URAG -> URAG: Implementing a Unified Hybrid RAG for Precise Answers in University Admission Chatbots -- A Case Study at HCMUT (2501.16276)
A hybrid system combining rule-based and RAG methods to improve lightweight LLMs for educational chatbots

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reacted to Reality123b's post with 👀 about 1 month ago

Post

1576

I got an issue with inference API

whatever model I choose, whatever input I give, it outputs "failed to fetch" in my laptop, pc, phone and every device, I tried different Accounts, etc. but still this error.

help me as my hf space (almost all of them) uses the API.

9 replies