By popular demand, we've automated the "turn the Module into a Keras Layer" step. So now you can just… use torch Modules in a Keras model/layer. No extra step needed. Just like you can use Keras models/layers as Modules.
OPEN SOURCE
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Jais Open Source Model Achieves Claude-Level Performance at Fraction Size
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Compared to closed source models, Jais approaches Claude level performance. This is remarkable considering it’s ~10th the size of large closed models.
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Jais Multilingual Model Outperforms Bloom and mT0
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In downstream evals, Jais easily tops the charts, beating prior open source models like Bloom and mT0. It’s easier to be good at two languages than 50!
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Meta’s Code Llama Models Now Available on Poe
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All @MetaAI
's #CodeLlama models are now available on Poe! @poe_platform > http://
poe.com – web – iOS – Android & MacOS -
MLFlow AI Gateway Now Powers RAG Applications with Llama2
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You can now use the highly-scalable #MLFlow AI Gateway for your RAG apps! This blog shows it all in action with a RAG application built using the API gateway, Llama2 and hosted models on MosaicML. Give it a read https://
bit.ly/3OTWA06 -
Community Thanks for Contributing Cool LoRA Models
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Thanks for contributing so many cool LoRAs , obrigado!
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Philippe’s Trolling Reshape Function Naming Convention
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looks like phillippe wanted to troll everyone by calling what should've been `tl.reshape` as `tl.view`.
It's like those trolly C macros ( #define float int ) -
BART Abstractive Text Summarization with KerasNLP and Keras Core
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Abstractive text summarization with BART — using KerasNLP and Keras Core, with JAX
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Arabic-English-Code Model Enables Multilingual Transfer Learning
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(2/2) Training on an Arabic-English-Code dataset has allowed for transfer learning from English to Arabic and also from Arabic to English. Access the model on Hugging Face: https://
huggingface.co/inception-mbzu
ai
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Jais Arabic LLM Outperforms LLaMa 13B Efficiently
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(1/2) Jais is the world's best-performing open-source Arabic language model. And in English, it performs as well as LLaMa 13B despite using a fraction of the training data and a fraction of the electricity to train. Contact us to learn more: http://
cerebras.net/contact-us