HuggingFace introduces Quanto, a Python quantization toolkit to reduce computational and memory costs of evaluating deep learning models. The toolkit supports embedding quantization, offering binary and scalar (int8) forms of quantization for float32 embedding values.
.@HuggingFace's PEFT library is now supported in 𝚖𝚕𝚏𝚕𝚘𝚠.𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜 flavor! 🚀 In addition, log any Pipeline type & skip copying foundational model weights for quicker, cost-effective development. Give the updated flavor a try today! ➡️ https://t.co/kEh0tml3Db https://t.co/laoUjlVJMl
HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce the Computational and Memory Costs of Evaluating Deep Learning Models #DL #AI #ML #DeepLearning #ArtificialIntelligence #ComputerVision #AutonomousVehicles #Robotics https://t.co/cSF3vPENzV
✨ sentence-transformers started supporting Embedding Quantization and GISTEmbedLoss 📌 Two forms of quantization exist at this time: binary and scalar (int8). These quantize embedding values from float32 into binary and int8, respectively. For Binary quantization, you can use… https://t.co/GuS1HK61kT
HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce the Computational and Memory Costs of Evaluating Deep Learning Models Quick read: https://t.co/LN6ZzVjpjw Github: https://t.co/CB3mJkxLOq #ArtificialIntelligence
Wondered how to use embedding quantization with @vespaengine? Here you go. Thanks @jobergum! https://t.co/r9MyZ0lbN9