🦙 Add llama fine-tuning notebook example #130
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baptistecolle
changed the title
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
tengomucho
approved these changes
Dec 18, 2024
| "The easiest approach to tune a model with SPMD is using Fully Sharded Data Parallel [(FSDP)](https://engineering.fb.com/2021/07/15/open-source/fsdp/). Pytorch/XLA most recent and performant implementation is [FSDP v2](https://github.com/pytorch/xla/blob/master/docs/fsdpv2.md), that allows to shard weights, activations and outputs.\n", | ||
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| "This example shows how to tune Meta's LLama2 and Llama3 models on single host TPUs. For information on TPUs architecture, you can consult the [documentation](https://cloud.google.com/tpu/docs/system-architecture-tpu-vm).\n", |
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Perhaps we can say "to tune one of Meta's Llama models"
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What does this PR do?
This PR modify the .md example into a juypter notebook for easier reproducibility of the fine-tuning
The documentation has been updated to point to the new notebook