[Download 541-page PDF eBook] Understanding #DeepLearning: http://
udlbook.github.io/udlbook/ by @SimonPrinceAI —————
#BigData #DataScience #AI #ML #MachineLearning #NeuralNetworks #ReinforcementLearning #NLProc #ComputerVision #Algorithms #DataScientists #Mathematics
OPEN SOURCE
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Free Deep Learning eBook by Simon Prince Available for Download
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Yi 34B Tech Report Reveals Model Architecture and Capabilities
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Wondering what's powering the trending #yi34b? Curious about the tech behind it? At @01AI_Yi
, we've written a tech report revealing the inside scoop on:
Base and chat models
200K long context model
Depth-upscaled model
Vision-language model
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Multilingual LLM Model Outperforms Google Gemma 2
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Leading in the OpenLLM leaderboard across multiple languages including English, German, Spanish, French, Italian, Dutch, and Portuguese, our model achieves top scores when ranked against larger sized models such as Google’s Gemma 2. (2/3)
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Stable LM 2 1.6B: State-of-the-art Small Language Model
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We’ve published our technical report on Stable LM 2 1.6B; our state-of-the-art small language model trained on multilingual data. This in-depth study covers the training methods utilized for both the base and instruction-tuned versions of the model. (1/3)
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Yi Open Foundation Models by 01.AI: Advanced Deep Learning
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Yi: Open Foundation Models by 01 . AI Young et al.: https://
arxiv.org/abs/2403.04652 #ArtificialIntelligence #DeepLearning #MachineLearning -
Hugging Face Becomes Critical Platform for AI Leaderboards
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Very cool to see that in the past year, @huggingface has become the platform for leaderboards, benchmark and evaluations, which are very critical pieces of the AI building process. You can find a lot of these like the chatbot Arena, the openLLM leaderboard, the bigcode
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Simple Multi-Backend Model for Sequence Similarity Estimation
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Here's a simple model I wrote yesterday (it learns to estimate a similarity metric between pairs of sequences). It runs with all backends — no code changes. Tried PT -> trains at 24ms/step on V100. Tried JAX -> trains at 10ms/step on V100.
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Keras and PyTorch: Prototype Once, Deploy Anywhere with JAX
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I know some companies like to prototype in PyTorch then rewrite in JAX for performance. Did you know you could just… prototype in Keras + PyTorch, then switch to JAX *while keeping all of your model code*? At most you'll have to rewrite the train_step or the training loop.
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Essential Keras Tips for Debugging and Prototyping with Different Backends
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Essential Keras tips: 1. Debug/prototype with eager execution. You can prototype with the PyTorch backend, or even with the NumPy backend if you're only looking at a layer or a forward pass (the NumPy doesn't support gradients/training). With JAX or TF, make sure to use
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01AI Yi Successfully Upscales Yi-6B to Yi-9B Model
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It's super exciting to see more success using DUS. @01AI_Yi Great job! Well done! "Method Following the methodology outlined by Kim et al. [38], our goal is to upscale our Yi-6B base model, which has 32 layers, to a 9B model named the Yi-9B base model, featuring 48