Good post. A lot of interest atm in wiring up LLMs to a wider compute infrastructure via text I/O (e.g. calculator, python interpreter, google search, scratchpads, databases, …). The LLM becomes the "cognitive engine" orchestrating resources, its thought stack trace in raw text
LLMS
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Request to integrate GPT-3 for text insertion in AI image generation
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While we’re on the subject, @Suhail if you guys could somehow leverage the GPT-3 language model to insert real words into the playground images, that would be
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DeepMind’s Epistemic Networks Reduce LLM Fine-Tuning Data Requirements
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DeepMind’s Epistemic Neural Networks Enable Large Language Model Fine-Tuning With 50% Less Data https://
syncedreview.com/2022/11/16/dee
pminds-epistemic-neural-networks-enable-large-language-model-fine-tuning-with-50-less-data/
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Using GPT-4 to enhance NPC behavior in video games
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GTP-4 integration will make NPCs in video games a whole lot more interesting
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Expert Choice: Novel Mixture-of-Experts Routing Algorithm
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Introducing a novel mixture-of-experts routing algorithm, called Expert Choice, that can achieve optimal load balancing between experts while allowing heterogeneous token-to-expert mapping. Learn how it’s done at https://
goo.gle/3OdpO9t -
LangChain 0.0.14 Release with GitHub Actions and Vector DB Improvements
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🦜🔗LangChain version 0.0.14
— LangChain (@LangChain) 16 novembre 2022
🧹Improve GitHub Actions (@PredragGruevski)
🎉Improve env var handling (@deliprao)
🥗Improve coloring of logginghttps://t.co/LuIrkDkbzM
Also, here's an example of using the new vector DB question/answering chainhttps://t.co/hPFqkC1l2cLangChain version 0.0.14 Improve GitHub Actions (
@PredragGruevski
)
Improve env var handling (
@deliprao
)
Improve coloring of logging https://
github.com/hwchase17/lang
chain
… Also, here's an example of using the new vector DB question/answering chain -
Hugging Face Releases 1.3T Parameter Mixture-of-Experts Model
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We’ve heard mixture-of-experts (MoE) were in the air (GPT4??) so we’ve just added the first one in the transformers library for you to play with 🙂 With for nothing less than a 1.3 trillion parameters checkpoint model on the hub! The largest model on the hub at the moment
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Self-examination in AI science models for error detection
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what would be cool with science models like Galactica would be to add self examination of the inputs like the coming Codex model, where the model can inspect it’s theorem proofs or essays and spot errors
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Galactica: Open-Source Large Language Model for Science
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Paper: https://
galactica.org/static/paper.p
df
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Github: https://
github.com/paperswithcode
/galai
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Demo: https://
galactica.org -
Galactica: 120B Scientific Language Model for Research
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"Galactica" is a large scientific language model with 120B parameters, that can be used for a lot of tasks including
⦿ Predicting citations
⦿ Generating literature review
⦿ Generating molecules ⦿ Generating Jupiter notebooks etc.,