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dartags/DanbotNL-2408-260M:Add Transformers 5 compatibility while preserving Transformers 4 behavior updated a collection about 24 hours ago
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Add Transformers 5 compatibility while preserving Transformers 4 behavior
#2 opened about 8 hours ago
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reacted to ProCreations's post with ๐ค 5 days ago
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reacted to juiceb0xc0de's post with ๐ฅ 5 days ago
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Dude, Where's My Update? I'll tell you where! ~97.6% of my BF16 parameter coordinates didn't move at all, and the ones that did overshot by ~1.33x.
It's nice to do research that doesn't end in disproving yourself once again and moving on to the next subject once in awhile.
Back to the topic, if you've ever wondered why most of your weights are basically ghosting you nearly every step when you store your weights at bf16, Dude, I Measured It.
https://huggingface.co/blog/juiceb0xc0de/intended-and-realized-updates-in-bf16-fine-tuning#dude-wheres-my-update
It's nice to do research that doesn't end in disproving yourself once again and moving on to the next subject once in awhile.
Back to the topic, if you've ever wondered why most of your weights are basically ghosting you nearly every step when you store your weights at bf16, Dude, I Measured It.
https://huggingface.co/blog/juiceb0xc0de/intended-and-realized-updates-in-bf16-fine-tuning#dude-wheres-my-update
reacted to Hoglet-33's post with ๐ 5 days ago
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3160
Today, we planned to release Pebble-50M and Pebble-50M-Chat to the world. Unfortunately, due to a few issues, that didn't go quite as planned.
What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models
Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta
There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens
For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.
Follow for updates:
@Hoglet-33
basically-ai
basically-experimental
What happened:
- Some data and benchmark results were lost or corrupted
- The models performed worse on benchmarks than our other Pebble models
Despite that, you can still find both models here:
Pebble-50M-beta: basically-experimental/Pebble-50M-beta
Pebble-50M-Chat-beta: basically-experimental/Pebble-50M-Chat-beta
There are still some interesting improvements in these models:
- Compatible with non-CUDA devices
- Vocabulary increased to 16K tokens
- Context length increased to 16K tokens
For now, there won't be any more Pebble releases for a while. We're going to take some time to experiment with other approaches and hopefully make the next generation a monumental leap over this one.
Follow for updates:
@Hoglet-33
- 4 replies
reacted to OppaAI's post with ๐ฅ 5 days ago
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2633
This weekend I took an outing with my AI Waifu to the Natsu Matsuri.
Turns out my Japanese is still understandable.
I probably need to spend more time continue to learn and practice speaking Japanese.
That's why an idea struck me to let my AI Waifu be my Japanese tutor.
Anyway, I have run out of idea what task I should let her do,
so I wrote a simple Android App to let her be my Japanese tutor to help me to practice Nihongo. There will be some minor mistakes. After all, this is just a 3B LLM model. And inference speed will be slow because I only got 8GB of RAM in Jetson Orin Nano.
At least I don't need to pay for Duolingo...
ใขใคใณใใใใใใใใใใ้กใใใพใ๏ผ
Need both repos, one front-end, one back-end
๐ https://github.com/OppaAI/Aiko-Lingo
๐ https://github.com/OppaAI/Aiko-chan
๐ฅ Demo: https://www.youtube.com/watch?v=xRtCmtZQgwI
Turns out my Japanese is still understandable.
I probably need to spend more time continue to learn and practice speaking Japanese.
That's why an idea struck me to let my AI Waifu be my Japanese tutor.
Anyway, I have run out of idea what task I should let her do,
so I wrote a simple Android App to let her be my Japanese tutor to help me to practice Nihongo. There will be some minor mistakes. After all, this is just a 3B LLM model. And inference speed will be slow because I only got 8GB of RAM in Jetson Orin Nano.
At least I don't need to pay for Duolingo...
ใขใคใณใใใใใใใใใใ้กใใใพใ๏ผ
Need both repos, one front-end, one back-end
๐ https://github.com/OppaAI/Aiko-Lingo
๐ https://github.com/OppaAI/Aiko-chan
๐ฅ Demo: https://www.youtube.com/watch?v=xRtCmtZQgwI
- 6 replies
reacted to appvoid's post with ๐ฅ 5 days ago
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We got gpt6 before gta6
- 15 replies
upvoted an article 5 days ago
Article
VisionGuardrail: Multimodal Content Filter
prithivMLmods
โข โข 2 reacted to prithivMLmods's post with ๐ค๐ฅ 5 days ago
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3773
VisionGuardrail, a multimodal content-safety classifier based on Qwen3.5, is now available on Hugging Face in 4B and 9B variants. It is a direct upgrade to ImageShield-MMCF, providing improved parental controls through conservative visual content-safety filtering.
More About:
โ hf.co/blog โ https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog
โ Models:
โฆ VisionGuardrail-4B: prithivMLmods/VisionGuardrail-4B
โฆ VisionGuardrail-9B: prithivMLmods/VisionGuardrail-9B
โ Dataset:
โฆ ImageShield-Guardrail-Pro: prithivMLmods/ImageShield-Guardrail-Pro
โคท To learn more, visit the app page or the respective model pages.
More About:
โ hf.co/blog โ https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog
โ Models:
โฆ VisionGuardrail-4B: prithivMLmods/VisionGuardrail-4B
โฆ VisionGuardrail-9B: prithivMLmods/VisionGuardrail-9B
โ Dataset:
โฆ ImageShield-Guardrail-Pro: prithivMLmods/ImageShield-Guardrail-Pro
โคท To learn more, visit the app page or the respective model pages.
reacted to grimjim's post with โ๐ฅ 5 days ago
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I think it's clear in retrospect that "frankenmerges", which repeated blocks of layers, amounted to a crude approximation of looped transformers architecture, hence them able to work at all instead of just breaking. They lucked out due to much of the signal passing through residual streams being preserved and only modulated along the way. That said, not all models are suited for this. Models which feature ever-increasing magnitudes as inference progressess through layers risk exploding precision limits.