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cppraiiย  updated a model 12 days ago
pseudolab/GR00T-N1.7-3B-OMX-PickupDolls-2Cam
cppraiiย  published a model 12 days ago
pseudolab/GR00T-N1.7-3B-OMX-PickupDolls-2Cam
juyoungmlย  authored a paper 14 days ago
On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists
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Nymboย 
posted an update 9 days ago
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5444
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
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cppraiiย 
updated a model 12 days ago
cppraiiย 
published a model 12 days ago
juyoungmlย 
authored a paper 14 days ago
cppraiiย 
updated a model 20 days ago
cppraiiย 
published a model 20 days ago
LeoKimDownyย 
updated a dataset 25 days ago
cetusianย 
posted an update 29 days ago
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127
most labs won't bother teaching a small model to write and think in real romanian. no invented words, no english leaking in mid-sentence, diacritics intact.

we did.

surogate 3.5 is out. 2B and 4B, apache 2.0. invented word-forms cut from 4 per 1k down to ~1.5, and it reasons in the language you prompt it in, every time.

intelligence should speak your language too.

the models:

surogate/Surogate-3.5-4B

surogate/Surogate-3.5-2B
kinam0252ย 
submitted a paper to Daily Papers about 1 month ago
LeoKimDownyย 
published a dataset about 1 month ago
LeoKimDownyย 
updated a dataset about 1 month ago
LeoKimDownyย 
published a dataset about 1 month ago
yunihgย 
updated a dataset about 2 months ago
yunihgย 
published a dataset about 2 months ago
Tonicย 
posted an update 3 months ago
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3134
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks ,

Turns out : if we predict ๐ŸŒ earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.

Sentinel-2 imagery ๐Ÿ›ฐ๏ธbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.

meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize ๐Ÿ“กearth-bound response .

I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.

At least that's the concept !

check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth


- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
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cppraiiย 
published a dataset 3 months ago
woojun-jungย 
authored a paper 3 months ago
Tonicย 
posted an update 3 months ago
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4398
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks,

since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !

Check this one out :
NuTonic/sat-bbox-metadata-sft-v1

the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .

hope you like it ! ๐Ÿš€
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Tonicย 
posted an update 3 months ago
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3708
๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ Hey there folks ,

I'm sharing huggingface's largest dataset of annotated statelite images today.

check it out here : NuTonic/sat-image-boundingbox-sft-full

I hope you like it , the idea is to be able to use this with small vision models ๐Ÿš€
Nymboย 
posted an update 5 months ago
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7827
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
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