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google-research / timesfm

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/
Apache License 2.0
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PyTorch Implementation Coming #119

Open jinan-zhou opened 3 months ago

jinan-zhou commented 3 months ago

Hey, guys!

I am Jinan, a MLE from Nutanix. I will build a PyTorch version of TimeFM, together with my colleagues @tanmayshishodia and @rghosh08. Would also be super happy to collaborate with anyone interested!

Stay tuned!

Kimyongmin1018 commented 3 months ago

Do you have any idea when that might be?

verigle commented 1 month ago

is there any plan to release finetune code of pytorch ?

jinan-zhou commented 3 weeks ago

We are standardizing the interface by making TimesFM part of Hugging Face. After this PR is merged, you will be able to apply all kinds of tools in the LLM ecosystem, including fine tuning libraries. Stay tuned!

rghosh08 commented 3 weeks ago

[like] Rajat Ghosh reacted to your message:

From: Jinan Zhou @.> Sent: Thursday, October 24, 2024 8:10:14 PM To: google-research/timesfm @.> Cc: Rajat Ghosh @.>; Mention @.> Subject: Re: [google-research/timesfm] PyTorch Implementation Coming (Issue #119)

We are standardizing the interface by making TimesFM part of Hugging Face. After this PR [github. com] is merged, you will be able to apply all kinds of tools in the LLM ecosystem, including fine tuning libraries. Stay tuned! — Reply to ZjQcmQRYFpfptBannerStart CAUTION: External Email

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We are standardizing the interface by making TimesFM part of Hugging Face. After this PR [github.com]https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_transformers_pull_34082&d=DwMFaQ&c=s883GpUCOChKOHiocYtGcg&r=LbuJq7qKD0rpn6BubJ2OiOxO-4i6iBtrASjLVP1l0Ls&m=ihrpX2r_TCpJW78B1ykY9eHrD90EyjIyLlvgNtfmtm169T7kiZDJsr8v6V3SFDAF&s=m_9M0UMTSCYyv3LvouikZnHoPK807ES8SPI2MzTt3aU&e= is merged, you will be able to apply all kinds of tools in the LLM ecosystem, including fine tuning libraries. Stay tuned!

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