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stefan-it
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stefan-it  updated a model 20 days ago
gerturax/gerturax-3
stefan-it  updated a model 20 days ago
gerturax/gerturax-2
stefan-it  updated a model 20 days ago
gerturax/gerturax-1
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stefan-it 
posted an update about 22 hours ago
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1274
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
stefan-it 
posted an update 4 days ago
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5009
She arrived 😍

[Expect more models soon...]
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stefan-it 
updated 3 models 20 days ago
stefan-it 
in gerturax/gerturax-3 21 days ago

Adding `safetensors` variant of this model
#1 opened 21 days ago by
SFconvertbot
stefan-it 
in gerturax/gerturax-2 21 days ago

Adding `safetensors` variant of this model
#1 opened 21 days ago by
SFconvertbot
stefan-it 
in gerturax/gerturax-1 21 days ago

Adding `safetensors` variant of this model
#1 opened 21 days ago by
SFconvertbot
stefan-it 
updated a Space 21 days ago
stefan-it 
published 3 models 21 days ago
stefan-it 
published a Space 21 days ago
cschroeder 
posted an update about 2 months ago
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🔥 𝐅𝐢𝐧𝐚𝐥 𝐂𝐚𝐥𝐥 𝐚𝐧𝐝 𝐃𝐞𝐚𝐝𝐥𝐢𝐧𝐞 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧: 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 — with a focus on, but not limited to, active learning. See the original post for details.

➡️ 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
cschroeder 
posted an update 2 months ago
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Here’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: 5–15 minutes
Deadline for participation: January 12, 2025

—

❤️ We’re seeking responses from across the globe! If you know 1–3 people who might qualify for this survey—particularly those in different regions—please share it with them. We’d really appreciate it!

#NLProc #ActiveLearning #ML
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cschroeder 
posted an update 2 months ago
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💡𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁: 𝗛𝗮𝘃𝗲 𝘆𝗼𝘂 𝗲𝘃𝗲𝗿 𝗵𝗮𝗱 𝘁𝗼 𝗼𝘃𝗲𝗿𝗰𝗼𝗺𝗲 𝗮 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗹𝗮𝗯𝗲𝗹𝗲𝗱 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗱𝗲𝗮𝗹 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗡𝗟𝗣 𝘁𝗮𝘀𝗸?

Are you working on Natural Language Processing tasks and have faced the challenge of a lack of labeled data before? 𝗪𝗲 𝗮𝗿𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗰𝗼𝗻𝗱𝘂𝗰𝘁𝗶𝗻𝗴 𝗮 𝘀𝘂𝗿𝘃𝗲𝘆 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.

👉 With only 5–15 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.

❤️How you can help even more: If you know others working on supervised learning and NLP, please share this survey with them—we’d really appreciate it!

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

#NLP #ML
stefan-it 
posted an update 3 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.

👉 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!

👉 Model Hub Link: https://huggingface.co./model-garden-lms

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

Made from Bavarian Oberland with ❤️ and 🥨.
cschroeder 
posted an update 3 months ago
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🐣 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 learning—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

👂 Try it out for yourself! We are eager to hear your feedback.
🔧 Share your small-text applications and experiments in the newly added showcase section.
🌟 Support the project by leaving a star on the repo!

#activelearning #nlproc #machinelearning
cschroeder 
posted an update 4 months ago
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#EMNLP2024 is happening soon! Unfortunately, I will not be on site, but I will present our poster virtually on Wednesday, Nov 13 (7:45 EST / 13:45 CEST) in Virtual Poster Session 2.

In this work, we leverage self-training in an active learning loop in order to train small language models with even less data. Hope to see you there!
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cschroeder 
posted an update 6 months ago
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⚖️ 𝐀𝐈 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐬 𝐂𝐨𝐩𝐲𝐫𝐢𝐠𝐡𝐭 𝐈𝐧𝐟𝐫𝐢𝐧𝐠𝐞𝐦𝐞𝐧𝐭

This bold claim is not my opinion, but it has been made in a recent "report" of a group, whose stance is recognizable in their name. It is roughly translated as "Authors' Rights Initiative". They published a report which was also presented before the EU Parliament according to the LinkedIn post below.

I am not really interested in politics, but as an EU citizen I am of course somewhat interested in a reasonable and practical version of the EU AI Act. Not saying there should not be rules around data and AI, but this report is obviously very biased towards one side.

While I think the report itself does not deserve attention, I post it in the hope that you find more examples, where they did not address the issue adequately. Feel free to add to my LinkedIn posts (where the original authors will see it) or here.

[en] Executive summary: https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringement/3b900058e6-1725460935/executive-summary_engl_final_29-08-2024.pdf
[de] Full report: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4946214

LinkedIn: https://www.linkedin.com/posts/activity-7238912869268959232-6cFx