LLM Chatbot Web UI This project is a Gradio-based chatbot application that leverages the power of LangChain and Hugging Face models to perform both conversational AI and PDF document retrieval. The chatbot is capable of handling text-based queries, generating responses based on
OPEN SOURCE
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PyTorch’s Functional Interface Design Evolution History
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fun fact: nn.functional wasn't part of the first design, and not part of the first 4 pre-releases; but then @szagoruyko5 convinced us that the functional interface in twitter/torch-autograd (in lua) was really nice to use for certain kinds of research; and that we must have
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Who knows real Gemini/Mixtral use cases with benchmarks?
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Does anyone know of companies with real use cases using Gemini or Mixtral, please? With a benchmark, advantages/disadvantages, that led to this choice? And when I say "real," I mean truly real. Because there are "French Tech" projects that claim to use Mixtral.
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Custom Callbacks and Async-Safe Logging in Training Batches
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Note that if you use custom callbacks that override `on_train_batch_end()`, you are at risk of forcing synchronous logging. Make sure to make your callbacks async-safe, and annotate them as such by setting `self.async_safe = True`
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Priompt: JSX-based prompt design tool for AI development
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GitHub – anysphere/priompt: Prompt design using JSX. https://
bit.ly/3XRekha
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
JAX and Keras offer superior large-scale model training performance
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In general, you will find that JAX (and in particular JAX + Keras for the super simple API) offers an exceptionally smooth and performant large-scale model training experience. Zero hassle and high utilization out of the box — there is no alternative that comes close to it.
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Keras JAX Data and Model Parallelism Configuration
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1. Configuring data parallelism for Keras models running on JAX (works with fit/evaluate/predict with 0 other changes to your code). All model state and incoming dataset are automatically distributed. 2. Configuring model parallelism + data parallelism while specifying the
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Meta Llama 3.2 Achieves Breakthrough Inference Speed Performance
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High speed alert! Experience fast #AI #inference on @AIatMeta
's Llama 3.2 1B & 3B with unrivaled performance all running at full-precision: 2470 tokens per sec on 1B 1566 tokens per sec on 3B Start developing -
gptme: Open-Source AI Agent Tool for Your Terminal
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Your terminal just got smarter! Introducing gptme: an open-source tool that let's you run AI Agent in your terminal with local tools Here's why it's a game-changer: Free and open-source Runs code in your local environment Reads, writes, and edits files Browses
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Linux Kernel Public Release Anniversary 1991
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#otd in 1991 the Linux kernel was publicly released by Helsinki grad student Linus Torvalds: https://
bit.ly/3N5sfLG