(4/n) Our library has been co-designed from the ground up to run both dense and #sparse training workloads. This enables practitioners to focus on what they do best: push the boundaries of AI research.
TOOLS
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Build AI Agents with Open Source Mixtral Models
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Build Agents with Open Source Models! Explore the latest in context-aware search with our new template by @tjaffri
, powered by @MistralAI 's open-source mixtral model & hosted on @FireworksAI_HQ . It searches academic papers on Arxiv out of the box, and customize it with -
Microsoft Copilot: AI Assistant Integration and Usage
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It’s using Copilot – http://
copilot.microsoft.com -
Google Assistant Integration Sets New AI Apart from GPT4
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Thanks for sharing. Pretty identical to my thoughts as well. I think the only major thing that sets it apart from GPT4 is the Google Assistant integration on Android. But I’ve yet to test it, and haven’t seen any real examples yet.
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Hugging Face Competitions: New Features and Enhanced Customization Options
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New Features in Hugging Face Competitions – support for multiple metrics
– custom metrics
– fully hidden test set
– custom hardware support: CPU, A10g, A100
– custom requirements support
– custom time-limit for submission runs
– support for not just submission.csv but any -
LlamaIndex Roadmap: New LLM Models, Vector Stores, Ingestion
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Brief roadmap for more LlamaIndex integrations: More LLM/Embedding models More vector stores Ingestion Pipeline Transformation We'll be doing a live webinar on 16th with @llama_index for more in-depth walkthrough. Have more ideas? Let us know below
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Flowise Expands LlamaIndex Integration for Framework Flexibility
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We are just getting started, there are still lots of LlamaIndex components to be implemented into Flowise. We want to give users ability to use different frameworks, leverage different LLM techniques and achieve best for their use cases. Docs:
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Sub-Question Query Engine RAG Technique Advanced Implementation
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Sub-Question Query Engine Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response This is an exciting one as we replicated the RAG technique used in http://
secinsights.ai by LlamaIndex -
Simple Chat Engine for User-AI Conversations
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Simple Chat Engine Handle back and forth conversations between user and AI