GERTuraX

non-profit

AI & ML interests

New German LMs

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stefan-itย  updated a model about 2 months ago
gerturax/gerturax-3
stefan-itย  updated a model about 2 months ago
gerturax/gerturax-2
stefan-itย  updated a model about 2 months ago
gerturax/gerturax-1
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stefan-itย 
posted an update 8 days ago
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Wohoo ๐Ÿฅณ I have finished my 2025 GPU workstation build and I am very excited to train new awesome open source models on it.

I built my last GPU workstation 5 years ago featuring an AMD Ryzen 5900X, 64GB of G.SKILL Trident Z RGB on an ASRock X570 Taichi cooled by an Alphacool Eisbรคr 420. GPU was a Zotac RTX 3090 AMP Extreme. Unfortunately, I was never satisfied with the case - some Fractal Define 7, as it is definitely too small, airflow is not optimal as I had to open the front door all the time and it also arrived with a partly damaged side panel.

For my new build, I've used the following components: an outstanding new AMD Ryzen 9950X3D with 64GB of Corsair Dominator Titanium (what a name). As a huge Noctua fan - warm greetings to my Austrian neighbors - I am using the brand new Noctua NH-D15 G2 on an ASRock X870E Taichi in an amazing Lian Li LANCOOL III chassis. One joke that only NVIDIA Blackwell users will understand: you definitely need a tempered glass panel to check if your GPU cables/connectors start melting ๐Ÿ˜‚ And the best is yet to come: I returned my previously bought Zotac RTX 5090 Solid to the eBay seller (because of... missing ROPs, only NVIDIA Blackwell users will again understand) and bought a Zotac 5090 AMP Extreme INFINITY (yes, the long name indicates that this is the flagship model from Zotac) from a more trustworthy source (NBB in Germany).

I am so happy to start training and fine-tuning new open source models - stay tuned!!!
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stefan-itย 
posted an update about 1 month ago
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๐Ÿ‡น๐Ÿ‡ท ๐Ÿ˜ I'm very happy to finally announce my new Turkish LM called "BERT5urk":

stefan-it/bert5urk

It is a 1.42B T5-based model, trained with UL2 pretraining objective on the Turkish part of the awesome HuggingFaceFW/fineweb-2 dataset.

Feel free to check it out!
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stefan-itย 
posted an update about 1 month ago
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After running some 3DMark and FurMark benchmarks on Windows to make sure that my new 5090 is not causing melting cables [1] and some nice shots with a thermal camera (I don't think that's too much), running some fine-tuning experiments with my favorite Flair & Transformers libraries are very easy to perform.

Important steps:

Good idea is to start with a fresh Ubuntu 24.04 installation with latest CUDA 12.8 and the open NVIDIA driver - follow more advices from [2]:

sudo apt -y install cuda-toolkit-12-8 nvidia-open

I tried update from an existing Ubuntu installation with an older CUDA and driver version and it resulted in a non-startable system.

If you are using PyTorch 2.6 with built CUDA 12.6 it will result in:

NVIDIA Graphics Device with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90.

But no worries! For PyTorch you need just to use a nightly 2.7 version that was built with CUDA 12.8. This can easily done via:

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128

After that the latest Flair version can be installed and fine-tuning will work!

References:

[1]: https://www.reddit.com/r/nvidia/comments/1inpox7/rtx_50_series_12vhpwr_megathread/
[2]: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network
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stefan-itย 
posted an update about 1 month ago
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5087
She arrived ๐Ÿ˜

[Expect more models soon...]
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stefan-itย 
in gerturax/gerturax-3 about 2 months ago
stefan-itย 
in gerturax/gerturax-2 about 2 months ago
stefan-itย 
in gerturax/gerturax-1 about 2 months ago
stefan-itย 
updated a Space about 2 months ago
stefan-itย 
published a Space about 2 months ago
cschroederย 
posted an update 3 months ago
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๐Ÿ”ฅ ๐…๐ข๐ง๐š๐ฅ ๐‚๐š๐ฅ๐ฅ ๐š๐ง๐ ๐ƒ๐ž๐š๐๐ฅ๐ข๐ง๐ž ๐„๐ฑ๐ญ๐ž๐ง๐ฌ๐ข๐จ๐ง: Survey on Data Annotation and Active Learning

Short summary: We need your support for a web survey in which we investigate how recent advancements in natural language processing, particularly LLMs, have influenced the need for labeled data in supervised machine learning โ€” with a focus on, but not limited to, active learning. See the original post for details.

โžก๏ธ Extended Deadline: January 26th, 2025.
Please consider participating or sharing our survey! (If you have any experience with supervised learning in natural language processing, you are eligible to participate in our survey.)

Survey: https://bildungsportal.sachsen.de/umfragen/limesurvey/index.php/538271
cschroederย 
posted an update 3 months ago
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Hereโ€™s just one of the many exciting questions from our survey. If these topics resonate with you and you have experience working on supervised learning with text (i.e., supervised learning in Natural Language Processing), we warmly invite you to participate!

Survey: https://bildungsportal.sachsen.de/umfragen/limesurvey/index.php/538271
Estimated time required: 5โ€“15 minutes
Deadline for participation: January 12, 2025

โ€”

โค๏ธ Weโ€™re seeking responses from across the globe! If you know 1โ€“3 people who might qualify for this surveyโ€”particularly those in different regionsโ€”please share it with them. Weโ€™d really appreciate it!

#NLProc #ActiveLearning #ML
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cschroederย 
posted an update 4 months ago
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๐Ÿ’ก๐—Ÿ๐—ผ๐—ผ๐—ธ๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐˜€๐˜‚๐—ฝ๐—ฝ๐—ผ๐—ฟ๐˜: ๐—›๐—ฎ๐˜ƒ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ ๐—ต๐—ฎ๐—ฑ ๐˜๐—ผ ๐—ผ๐˜ƒ๐—ฒ๐—ฟ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐—น๐—ฎ๐—ฐ๐—ธ ๐—ผ๐—ณ ๐—น๐—ฎ๐—ฏ๐—ฒ๐—น๐—ฒ๐—ฑ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐˜๐—ผ ๐—ฑ๐—ฒ๐—ฎ๐—น ๐˜„๐—ถ๐˜๐—ต ๐—ฎ๐—ป ๐—ก๐—Ÿ๐—ฃ ๐˜๐—ฎ๐˜€๐—ธ?

Are you working on Natural Language Processing tasks and have faced the challenge of a lack of labeled data before? ๐—ช๐—ฒ ๐—ฎ๐—ฟ๐—ฒ ๐—ฐ๐˜‚๐—ฟ๐—ฟ๐—ฒ๐—ป๐˜๐—น๐˜† ๐—ฐ๐—ผ๐—ป๐—ฑ๐˜‚๐—ฐ๐˜๐—ถ๐—ป๐—ด ๐—ฎ ๐˜€๐˜‚๐—ฟ๐˜ƒ๐—ฒ๐˜† to explore the strategies used to address this bottleneck, especially in the context of recent advancements, including but not limited to large language models.

The survey is non-commercial and conducted solely for academic research purposes. The results will contribute to an open-access publication that also benefits the community.

๐Ÿ‘‰ With only 5โ€“15 minutes of your time, you would greatly help to investigate which strategies are used by the #NLP community to overcome a lack of labeled data.

โค๏ธHow you can help even more: If you know others working on supervised learning and NLP, please share this survey with themโ€”weโ€™d really appreciate it!

Survey: https://bildungsportal.sachsen.de/umfragen/limesurvey/index.php/538271
Estimated time required: 5โ€“15 minutes
Deadline for participation: January 12, 2025

#NLP #ML
stefan-itย 
posted an update 4 months ago
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My latest project is the outcome of the last 2+ years working with TPUs from the amazing TPU Research Cloud (TRC) program and training Encoder-only LMs with the TensorFlow Model Garden library.

๐Ÿ‘‰ Link: https://github.com/stefan-it/model-garden-lms

An overview of some features:

- Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden
- Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models
- Supported architectures include BERT, BERT with Token Dropping and TEAMS

I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come!

๐Ÿ‘‰ Model Hub Link: model-garden-lms

If you find these resources useful, please give them a like!

Made from Bavarian Oberland with โค๏ธ and ๐Ÿฅจ.
cschroederย 
posted an update 4 months ago
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๐Ÿฃ New release: small-text v2.0.0.dev1

With small language models on the rise, the new version of small-text has been long overdue! Despite the generative AI hype, many real-world tasks still rely on supervised learningโ€”which is reliant on labeled data.

Highlights:
- Four new query strategies: Try even more combinations than before.
- Vector indices integration: HNSW and KNN indices are now available via a unified interface and can easily be used within your code.
- Simplified installation: We dropped the torchtext dependency and cleaned up a lot of interfaces.

Github: https://github.com/webis-de/small-text

๐Ÿ‘‚ Try it out for yourself! We are eager to hear your feedback.
๐Ÿ”ง Share your small-text applications and experiments in the newly added showcase section.
๐ŸŒŸ Support the project by leaving a star on the repo!

#activelearning #nlproc #machinelearning