NousResearch/DeepHermes-3-Llama-3-8B-Preview
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Junlin Zhou
jlzhou
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Distributed SFT with trl and DeepSpeed Part 2: Scaling Locally
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I'm glad you found it helpful!
Yes, this is planned. I was originally planning to write an article about training with the training operator, but now I'm wondering if I should skip that and focus on training with the new trainer instead.
PS: Kubeflow is migrating their training component from v1 (Kubeflow Training Operator) to v2 (Kubeflow Trainer).
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π’ New Research Alert: Making Language Models Smaller & Smarter!
Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance.
The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena.
π Key Findings:
β’ 77% parameter reduction.
β’ Maintained model capabilities.
β’ Improved generalization.
Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT
Code: https://github.com/joaopauloschuler/less-parameters-llm
Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance.
The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena.
π Key Findings:
β’ 77% parameter reduction.
β’ Maintained model capabilities.
β’ Improved generalization.
Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT
Code: https://github.com/joaopauloschuler/less-parameters-llm
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Distributed SFT with trl and DeepSpeed Part 2: Scaling Locally
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