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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone
Falcon 180B: Model Overview
James Briggs · 2023-09-07 · via Pinecone

Falcon 180B is a 180-billion-parameter Large Language Model (LLM). It has comparable performance to Google's PaLM 2 (Bard) and is not far behind GPT-4.

It is touted as the "Llama 2" killer due to its higher performance as a pretrained-only model. As of September 2023, Falcon 180B ranked as the highest-performing pretrained LLM on the Hugging Face Open LLM Leaderboard.

The model is big. Inference requires 640GB of memory — a mere eight A100 80GB GPUs — when quantized to half-precision (FP16). Alternatively, we can quantize down to int4, requiring eight A100 40GB GPUs (320GB memory). That can easily put you back $20K / month if you keep it online.

For some, this price tag may be worth it. Falcon 180B's license permits commercial usage and allows organizations to keep their data on their chosen infrastructure, control training, and maintain more ownership over their model than alternatives like OpenAI's GPT-4 can provide.

Performance-wise, Falcon 180B is impressive. It is the highest-performing open-access LLM and is comparable the PaLM-2 Large (which powers Bard).

Falcon 180B performance against PaLM models, source [1].

Compared to OpenAI's models, Falcon 180B outperforms GPT-3.5 on some benchmarks. For the majority of benchmarks, Falcon 180B scores between GPT-3.5 and GPT-4 [1].

Video walkthrough of the Falcon 180B LLM.

We will be adding further information and guides surrounding Falcon 180B to this page — so stay tuned!


References

[1] P. Schmid, O. Sanseviero, P. Cuenca, L. von Werra, J. Launay, Spread Your Wings: Falcon 180B is here (2023), Hugging Face Blog