Reduce training cost of diffusion models by ~70% through masked training of transformer backbones. Masked training is popular for self-supervised representation learning, but we are first to show for #GenerativeAI https://
github.com/Anima-Lab/Mask
DiT
… @wn8_nie @Kay12400259 @ArashVahdat
CODE
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Masked Training Reduces Diffusion Model Training Costs by 70%
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CPU Performance Progress: Fast MLP Training on Modern MacBooks
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I actually recently marveled at how far CPU performance has come (comparing my undergrad computer to a modern MacBook). Was creating a confidence interval demo with a simple sklearn MLP, and it fits 500 iters in like a second on a 2020 MacBook Air: https://
github.com/rasbt/MachineL
earning-QandAI-book/blob/main/supplementary/q25_confidence-intervals/1_four-methods.ipynb
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Functions Usage Experience Webinar with DBuniatyan
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This is great exploration of `functions` usage!
— Harrison Chase (@hwchase17) 19 juin 2023
Excited to add @DBuniatyan to our functions webinar this Wednesday to talk through his experience using it! https://t.co/t1n8o6rX2bThis is great exploration of `functions` usage! Excited to add @DBuniatyan to our functions webinar this Wednesday to talk through his experience using it!
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Loading Documents Into Structured Tools Feature Development
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Wdym loading documents into structured tools? Gonna add something like this today/tmrw as well!
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Question Answering with Citations Using LangChain Functions
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Question Answering with citations Ahead of our webinar on Wednesday, more `functions` goodness from @jxnlco
: Answer a question (with citations) from a piece of context. Uses `functions` to specify the return schema of the answer + supporting facts https://
python.langchain.com/docs/modules/c
hains/additional/qa_citations
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Deploy Computer Vision Apps with Hugging Face Spaces and Gradio
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🎥 New Video Alert!https://t.co/PkU9Felksh
— Satya Mallick (@LearnOpenCV) 19 juin 2023
Unlock the secrets of deploying a Computer Vision App in our latest video!
🚀We simplify the process with Hugging Face Spaces and Gradio. Tune in to elevate your #techgame! #ComputerVision #AppDeployment #HuggingFace #Gradio #ai… pic.twitter.com/aKydWO9VGiNew Alert! https://
youtube.com/watch?v=6b3S2D
2TiAo
… Unlock the secrets of deploying a Computer Vision App in our latest video! We simplify the process with Hugging Face Spaces and Gradio. Tune in to elevate your #techgame! #ComputerVision #AppDeployment #HuggingFace #Gradio #ai -
Memory Types and Reflection for Improved Chatbot Performance
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The most interesting thing here IMO is the exploration of multiple different types of "memory" for creating chatbots
— Harrison Chase (@hwchase17) 19 juin 2023
Baseline would just be retrieval over the raw corpus, but by doing some reflection-like things as a preprocessing step, you can get better results https://t.co/XKeHg3Rgp0The most interesting thing here IMO is the exploration of multiple different types of "memory" for creating chatbots Baseline would just be retrieval over the raw corpus, but by doing some reflection-like things as a preprocessing step, you can get better results
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Webinar on Functions: Use Cases and Q&A Session
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To hear more about `functions`, join us (me, Atty, @fpingham
, and @jxnlco
) for an exciting webinar this Wednesday! We'll cover how to use it, what some common use cases are, and then answer any and all questions! -
LLM Functions and Pydantic Schema Parsing Integration
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llm_kwargs: this is where we specify `functions` and `function_call` output_parser: this is where we parse the `function_call` response into either a string, json object, or pydantic object @pydantic is really nice for letting users specify schema in a Pythonic way!
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Adding Function Chains: Extraction, Tagging, Question-Answering
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So that's the general formula for how we're adding `functions` chains So far we've added: – Extraction
– Tagging
– Question-Answering with citations We're EXTREMELY open to contributions here – with this formula should be a pretty easy addition