Spaces:
Running
Running
File size: 1,439 Bytes
3893779 2009ed1 18a0c21 6fc11e4 18a0c21 2009ed1 d6140d2 d5dc66d 2009ed1 62d29da 2009ed1 6b2edfb 2009ed1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 |
---
title: README
emoji: 🚀
colorFrom: indigo
colorTo: blue
sdk: static
pinned: false
---
# Foundation Model Stack
Foundation Model Stack (fms) is a collection of components developed out of IBM Research used for development, inference, training, and tuning of foundation models leveraging PyTorch native components.
## Optimizations
In FMS, 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:
- fully compilable models with no graph breaks
- full tensor-parallel support for all applicable modules developed in fms
- training scripts leveraging FSDP
- state of the art light-weight speculators for improving inference performance
## Usage
FMS is currently being deployed in [Text Generation Inference Server](https://github.com/IBM/text-generation-inference)
## Repositories
- [foundation-model-stack](https://github.com/foundation-model-stack/foundation-model-stack): Main repository for which all fms models are based
- [fms-extras](https://github.com/foundation-model-stack/fms-extras): New features staged to be integrated with foundation-model-stack
- [fms-fsdp](https://github.com/foundation-model-stack/fms-fsdp): Pre-Training Examples using FSDP wrapped foundation models
- [fms-hf-tuning](https://github.com/foundation-model-stack/fms-hf-tuning): Basic Tuning scripts for fms models leveraging SFTTrainer
|