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#
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## Optimizations
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In
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- fully compilable models with no graph breaks
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- full tensor-parallel support for all applicable modules developed in fms
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## Usage
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## Repositories
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- [foundation-model-stack](https://github.com/foundation-model-stack/foundation-model-stack): Main repository for which all
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- [fms-extras](https://github.com/foundation-model-stack/fms-extras): New features staged to be integrated with
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- [fms-fsdp](https://github.com/foundation-model-stack/fms-fsdp): Pre-Training Examples using FSDP wrapped foundation models
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- [fms-hf-tuning](https://github.com/foundation-model-stack/fms-hf-tuning): Basic Tuning scripts for
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# IBM AI Platform
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IBM's AI Platform is a collection of components developed out of IBM Research used for development, inference, training, and tuning of foundation models leveraging PyTorch native components.
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## Optimizations
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In this platform, we aim to bring the latest optimizations for pre-training/inference/fine-tuning to all of our models. A few of these optimizations include, but are not limited to:
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- fully compilable models with no graph breaks
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- full tensor-parallel support for all applicable modules developed in fms
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## Usage
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Components such as speculative decoding have been deployed to [vLLM](https://docs.vllm.ai/en/latest/getting_started/examples/mlpspeculator.html)
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## Repositories
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- [foundation-model-stack](https://github.com/foundation-model-stack/foundation-model-stack): Main repository for which all AI platform models are based
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- [fms-extras](https://github.com/foundation-model-stack/fms-extras): New features staged to be integrated with our AI platform
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- [fms-fsdp](https://github.com/foundation-model-stack/fms-fsdp): Pre-Training Examples using FSDP wrapped foundation models
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- [fms-hf-tuning](https://github.com/foundation-model-stack/fms-hf-tuning): Basic Tuning scripts for AI platform models leveraging SFTTrainer
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