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The T5 model (Raffel et al, 2019) is widely used in the NLP community. Its base model has been downloaded from Hugging Face millions of times, leaving no doubt that these models are a favorite of the community. However, T5's tokenizer omits important code-related tokens and subsequent pretraining datasets have been released with higher quality filtering and more diverse domains. In this blog post, we introduce a new version of T5 intended to address those weaknesses: Pile-T5, trained on the Pile (Gao et al, 2020) and using the LLaMA tokenizer (Touvron et al, 2023). Model Description# Our alternative version replaces…

Scaling machine learning (ML) experiments is a challenging process that requires efficient resource management, experiment tracking, and infrastructure scalability. neptune.ai offers a centralized platform to manage ML experiments, track real-time model performance, and store metadata. Kubernetes automates container orchestration, improves resource utilization, and enables horizontal and vertical scalability. Combining neptune.ai and Kubernetes provides a robust solution for scaling ML experiments, making it easier to manage and scale experiments across multiple environments and team members. Scaling machine-learning experiments efficiently is a challenge for ML teams. The complexity lies in managing configurations, launching experiment runs, tracking their outcomes, and optimizing resource allocation.…