Besides the previously discovered "Magic Draft" feature, NotebookLM may get a bot creation UI where you can set a custom prompt on top of uploaded Notebook sources. These chatbots are meant to be sharable and potentially "embedded" This will turn them into Gems 2.0!
LLMS
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Google NotebookLM to potentially enable custom chatbot creation
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BREAKING 🚨: Google’s NotebookLM could let users build custom chatbots from notebooks.
— 🚨 AI News | TestingCatalog (@testingcatalog) 30 septembre 2024
If you already had high expectations from NotebookLM, you must raise them even higher! Here is why 👇
Disclaimer: All mentioned features here are WIP 🚧
h/t @bedros_p pic.twitter.com/Nl8XagjLniBREAKING : Google’s NotebookLM could let users build custom chatbots from notebooks. If you already had high expectations from NotebookLM, you must raise them even higher! Here is why Disclaimer: All mentioned features here are WIP h/t @bedros_p
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Open NotebookLM: Convert PDFs to Podcasts with AI
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Open NotebookLM Convert your PDFs into podcasts with open-source AI models (Llama 3.1 405B and MeloTTS). https://
huggingface.co/spaces/gabriel
chua/open-notebooklm
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PDF2Audio: Convert PDFs to Podcasts and Audio Summaries
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PDF2Audio Convert PDFs into an audio podcast, lecture, summary and others https://huggingface.co/spaces/lamm-mit/PDF2Audio
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SFT vs DPO: Finding the Sweet Spot in Model Training
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In my experience, SFT is quite destructive, which is why I like DPO better. There might be a sweet spot with SFT and low LRs though. I haven't experimented with it that much tbh.
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Testing Liquid AI Foundation Models Across Multiple Platforms
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You can test LFMs today using the following links:
– Liquid AI Playground: https://
playground.liquid.ai
– Lambda: https://
lambda.chat/chatui/models/
/models/LiquidCloud
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– Perplexity: https://
labs.perplexity.ai If you're interested, find more information in our blog post: -
LFM Architecture Enables New Foundation Model Design Space
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The LFM architecture opens a new design space for foundation models. This is not restricted to language, but can be applied to other modalities: audio, time series, images, etc. It can also be optimized for specific platforms, like @Apple
, @AMD
, @Qualcomm
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LFM-3B Achieves 32K Context Window with Promising RULER Scores
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In this preview release, we focused on delivering the best-in-class 32k context window. These results are extremely promising, but we want to expand it to very, very long contexts. Here are our RULER scores (
https://
github.com/hsiehjackson/R
ULER
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LFM Architecture: Memory-Efficient LLM for Long Contexts
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The LFM architecture is also super memory efficient. While the KV cache in transformer-based LLMs explodes with long contexts, we keep it minimal, even with 1M tokens. This unlocks new applications, like document and book analysis, directly in your browser or on your phone.
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Liquid AI Launches Three LLMs with SOTA Performance and Edge Optimization
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This is the proudest release of my career 🙂 At @liquidai
, we're launching three LLMs (1B, 3B, 40B MoE) with SOTA performance, based on a custom architecture. Minimal memory footprint & efficient inference bring long context tasks to edge devices for the first time!