2 repositorios
Awesome GitHub RepositoriesAll-Sample Processors
Logic applied to every generated sample, including those filtered from training, for logging, analysis, or statistics computation.
Distinct from Post-Generation Sample Processing: Distinct from Post-Generation Sample Processing: processes all samples unconditionally, not just those used for training.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · All-Sample Processors. Refine with filters or upvote what's useful.
Awesome All-Sample Processors GitHub Repositories
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- facebookresearch/mmf
facebookresearch/mmf
5,635Ver en GitHubMMF is a modular framework for building, training, and evaluating vision-and-language models. It provides a configuration-driven experiment system where model, dataset, and training parameters are defined through composable YAML files, alongside a curated model zoo of pretrained checkpoints for state-of-the-art multimodal architectures. The framework includes a multimodal dataset loader that downloads, processes, and batches vision-and-language data, and a vision-language model trainer supporting distributed training, mixed precision, and checkpoint-based resumption. The framework distinguish
Transforms raw data samples into model-ready dictionaries using custom callables.
Pythoncaptioningdeep-learningdialog - thudm/slime
THUDM/slime
4,259Ver en GitHubSLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The fra
Processes every generated sample, including those filtered from training, for logging, analysis, or statistics computation.
Python
