Multi-Agent RAG Online Workshop If there’s anything better than agentic RAG, it’s multi-agent RAG! In this event, we’ll explore the big idea behind “multi-agent” applications. These types of workflows combine multiple independent agents, which can be structured to work
LLMS
-
LangChain Named MongoDB’s AI App Framework Partner of Year
By
–
We’re thrilled to be MongoDB's AI App Framework Partner of the Year! LangChain is the #1 choice for developers building GenAI apps. We have immense gratitude for our community of over 2,000 contributors who have helped us shape the LLM application development space, and are
-
RAG versus fine-tuning: Understanding key AI training approaches
By
–
That sounds like RAG, not fine-tuning
-
GPU and Disk Constraints: Need for Smaller Dataset Variants
By
–
I'm not only GPU poor but disk poor too. 350GB?
(And ofc doing so wouldn't be representative of the full data distribution)
Also while replying, ideally there could be a "dataset miniseries", e.g. 1B, 10B, 100B, and then full. I think would be very helpful and bandwidth saving. -
llm.c Day 24: Multi-GPU Training in C/CUDA Outperforms PyTorch
By
–
Day 24 of llm.c: we now do multi-GPU training, in bfloat16, with flash attention, directly in ~3000 lines of C/CUDA, and it is FAST! We're running ~7% faster than PyTorch nightly, with no asterisks, i.e. this baseline includes all modern & standard bells-and-whistles: mixed
-
Weak Supervision in AI: Automation and LLM Integration
By
–
Explore the future of weak supervision in AI and get in-depth insights on automating the weak supervision pipeline, multimodal integration, and the intersection of weak supervision with large language models. https://
buff.ly/4b0h9BS -
Building the Fastest and Most Efficient Fine-Tuning Stack
By
–
What does it take to build the #fastest, most #efficient fine-tuning stack? We got answers! Join our deep dive to learn: Latest fine-tuning #optimization techniques Metric driven analysis of each optimization How to get started on your own https://
pbase.ai/3Wq2ATG -
Cohere Build Day Toronto: Knowledge Agents with Command R
By
–
Cohere Build Day is in Toronto! Follow us as we build knowledge agents with Command R and R+. This is your chance to share your projects, ask our team questions, and leave feedback.
-
Build RAG System Llama 3B-Instruct PDFs FAISS
By
–
Build a RAG system with Llama 3B-Instruct for your PDFs Uses API for partitioning & chunking, FAISS for vector store, huggingface for the model It's a collab notebook so it's easy to get started! Thanks @mariaKhalusova for a great resource! https://
colab.research.google.com/drive/1BJYYyrP
Ve0_9EGyXqeNyzmVZDrCRZwsg?usp=sharing#scrollTo=Y2m2l-vt_RSp
… -
How Large Language Models Work: Visual Explanation
By
–
How LLMs work, clearly explained with visuals: pic.twitter.com/ujKeJrw5Lf
— Sumanth (@Sumanth_077) 3 mai 2024How LLMs work, clearly explained with visuals: