Moderation To prevent harmful content being passed to LLM, we have added Moderation nodes. Input will be checked against violation rules, and an error message will be shown accordingly.
AI
-
Claude 2.1 Launches 200K Context Window Support
By
–
💬 Claude 2.1 support
— FlowiseAI (@FlowiseAI) 24 novembre 2023
Supporting @AnthropicAI 200K Context Claude 2.1 model pic.twitter.com/fIhdxb0k9YClaude 2.1 support Supporting @AnthropicAI 200K Context Claude 2.1 model
-
Flowise v1.4.3 Release: New APIs, Claude 2.1 and MongoDB Support
By
–
Friday release time
Flowise v1.4.3 freshly baked with: Upsert API Moderation Claude 2.1 support Langfuse User Tracing Vectara QA Chain MongoDB Atlas Chatflow UI/UX -
New Upsert API for Vector Embeddings Management
By
–
Upsert API We now expose a new endpoint for upserting vector embeddings:
/api/v1/vector/upsert Previously, a slight change on the flow causes everything to be upserted again. With this change, users have more control on when/how to upsert. -
Sam Altman’s Tweet Compared to Historical Coup Consequences
By
–
Sam Altman's tweet is deceptive. Failed coup attempts are always dealt with in the same way throughout world history. Remember how Putin dealt with Prigozhin? Corporate America is no different when it comes to executing (figuratively speaking) people involved in a coup.
-
LLM Reliability: Replacing Auto-Regressive Prediction with Planning
By
–
Please ignore the deluge of complete nonsense about Q*.
One of the main challenges to improve LLM reliability is to replace Auto-Regressive token prediction with planning. Pretty much every top lab (FAIR, DeepMind, OpenAI etc) is working on that and some have already published -
LLM Fine-Tuning: Deep Dive Video Tutorial
By
–
Dive into LLM fine-tuning in my recent video: https://
youtu.be/7mOD9kgqRf0 -
Fine-tuning and RAG: Improving AI Reliability and Reducing Hallucinations
By
–
Fine-tuning helps but isn't a cure-all for AI issues, such as occasional 'hallucinations'. It improves knowledge robustness, and combining it with methods like Retrieval Augmented Generation (RAG) can increase the accuracy and reliability of your models.
-
Specificity Improves Language Models for Finance and Medical Tasks
By
–
Specificity enhances complex tasks, like updating a language model with finance data for better financial advice, or medical info for improved health responses.
-
Fine-Tuning Large Language Models: From Generalists to Specialists
By
–
The Art of Fine-Tuning Large Language Models… Fine-tuning transforms large language models like GPT-4 from generalists to specialists, like going from a broad education to a PhD, enabling expert-level performance in fields like medicine or finance.