this is a great and really unique description of how SVD works. never heard this before (from http://
jeremykun.com/2016/04/18/sin
gular-value-decomposition-part-1-perspectives-on-linear-algebra
…)
CODE
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Understanding SVD: A Unique Mathematical Perspective Explained
By
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RAG Query Translation Step-Back Prompting Technique
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RAG From Scratch: Query Translation (Step-Back) Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is the fourth in our videos on Query Translation, focused on step-back prompting from @denny_zhou
's group at DeepMind. -
Learn GenAI App Development with JavaScript and LangChain
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Want to learn how to build GenAI apps with JavaScript? @adamcowley will be teaching the "Learn with Jason" (
@LWJShow
) how to build their own custom apps using LangChain JS Happening LIVE tomorrow! Sign up here: https://
youtube.com/watch?v=sMTCGF
rAo08
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Google Gemma Launch Partner Delivers Optimized LLM Desktop Models
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Announced today, we are collaborating as a launch partner with @Google in delivering Gemma, an optimized series of models that gives users the ability to develop with #LLMs using only a desktop #RTX GPU.
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Actor Reflection with Episodic Memory for Agent Learning
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2/ Reflexion The actor reflects on each response, using citations for more actionable critique. Reflections stored in chat history as an "episodic memory buffer" to help the agent learn from past iterations. This better steers the generator in responding to the feedback.
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Language Agent Tree Search: Balancing Exploration and Reward
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3/ Language Agent Tree Search (LATS) Algorithm combines reflection and Monte-Carlo tree search to find the “best” trajectory for a task. The search process balances exploration and expected reward. It has 4 main steps: 1. Select: pick the best next actions based on the
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LLM Reflection Architecture: Generator and Reflector Pattern
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1/ Basic Reflection Two LLM nodes: a generator and a reflector. The generator tries to answer the request, while the reflector offers constructive criticism. Python: https://
github.com/langchain-ai/l
anggraph/blob/main/examples/reflection/reflection.ipynb
…
Youtube: https://
youtube.com/watch?v=v5ymBT
XNqtk&t=48s
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Google Launches Gemma Open Source Models Built on Gemini Technology
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It certainly has been a fun year @Google
: enjoy playing with our open source models Gemma, built from the same research and technology used to create the Gemini models. Blog: https://
blog.google/technology/dev
elopers/gemma-open-models/
…
Tech report: https://
storage.googleapis.com/deepmind-media
/gemma/gemma-report.pdf
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Google Releases Gemma Lightweight Open Source AI Models
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We have a long history of supporting responsible open source & science, which can drive rapid research progress, so we’re proud to release Gemma: a set of lightweight open models, best-in-class for their size, inspired by the same tech used for Gemini https://
blog.google/technology/dev
elopers/gemma-open-models/
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New HF Space for visualizing chunk splitting methods in RAG
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I've built a new HF Space to let you visualize how different splitting methods affect the chunks you fet for RAG! Try it out here: https://
huggingface.co/spaces/m-ric/c
hunk_visualizer
… It's heavily inspired from @GregKamradt 's http://
chunkviz.com – all credits to him for the idea!