TTT is a technique that allows artificial intelligence models to adapt and learn while in use, rather than just during pre-training.
The main advantage of TTT is that it can efficiently process long contexts (large amounts of input data) without significantly increasing the computational cost.
The researchers conducted experiments on various datasets, including books, and found that TTT often outperformed traditional methods.
In comparative benchmarks with other popular machine learning methods such as transformers and recurrent neural networks, TTT was found to perform better on some tasks.
This revolutionary method will bring us closer to creating more flexible and efficient artificial intelligence models that can better adapt to new data in real time.
Adaptations of the method have been published on Github:
- adaptation for Pytorch
- adaptation to JAX
#Pytorch #Jax #TTT #LLM #Training
https://t.iss.one/DataScienceT
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👍2
The Hundred-Page Language Models Book
Read it:
https://github.com/aburkov/theLMbook
Read it:
https://github.com/aburkov/theLMbook
#LLM #NLP #ML #AI #PYTHON #PYTORCH
https://t.iss.one/DataScienceM
👍4
Forwarded from Python | Machine Learning | Coding | R
Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".
https://www.k-a.in/pyt-transformer.html
This guide offers:
By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.
#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks
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👍1
✨PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
📝 Summary:
PyTorch FSDP is an industry-grade solution for efficient and scalable large model training. It enables significantly larger models with near-linear TFLOPS scalability, making advanced capabilities more accessible.
🔹 Publication Date: Published on Apr 21, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2304.11277
• PDF: https://arxiv.org/pdf/2304.11277
• Github: https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/fully_sharded_data_parallel.py
🔹 Models citing this paper:
• https://huggingface.co/databricks/dbrx-instruct
• https://huggingface.co/databricks/dbrx-base
• https://huggingface.co/Undi95/dbrx-base
✨ Spaces citing this paper:
• https://huggingface.co/spaces/nanotron/ultrascale-playbook
• https://huggingface.co/spaces/Ki-Seki/ultrascale-playbook-zh-cn
• https://huggingface.co/spaces/Gantrol/ultrascale-playbook-zh-cn
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #FSDP #DeepLearning #DistributedTraining #LargeModels
📝 Summary:
PyTorch FSDP is an industry-grade solution for efficient and scalable large model training. It enables significantly larger models with near-linear TFLOPS scalability, making advanced capabilities more accessible.
🔹 Publication Date: Published on Apr 21, 2023
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2304.11277
• PDF: https://arxiv.org/pdf/2304.11277
• Github: https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/fully_sharded_data_parallel.py
🔹 Models citing this paper:
• https://huggingface.co/databricks/dbrx-instruct
• https://huggingface.co/databricks/dbrx-base
• https://huggingface.co/Undi95/dbrx-base
✨ Spaces citing this paper:
• https://huggingface.co/spaces/nanotron/ultrascale-playbook
• https://huggingface.co/spaces/Ki-Seki/ultrascale-playbook-zh-cn
• https://huggingface.co/spaces/Gantrol/ultrascale-playbook-zh-cn
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #FSDP #DeepLearning #DistributedTraining #LargeModels
arXiv.org
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine...
✨PyTorch Distributed: Experiences on Accelerating Data Parallel Training
📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.
🔹 Publication Date: Published on Jun 28, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC
📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.
🔹 Publication Date: Published on Jun 28, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC