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mmhamdyΒ 
posted an update 37 minutes ago
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Things rarely go as we expect!

In 2017, Google released the Transformer architecture. While it was clear the model was promising, absolutely no one (including its authors) anticipated the pervasive global revolution it would create!

The authors actually viewed the Transformer as just a stepping stone for a much more ambitious project: The MultiModel.

Their ultimate goal was to build a single deep learning architecture capable of jointly learning massive, diverse tasks across entirely different domains (in 2017). A One Model To Learn Them All.

In fact, the MultiModel paper was published in the exact same month as Attention Is All You Need!

But history had other plans. The building block eclipsed the grand design!

So, have you heard about the MultiModel before? πŸ˜€
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alielfilali01Β 
posted an update about 6 hours ago
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Plans in HTML > Plans in Markdown
ReubencfΒ 
posted an update 10 days ago
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I have improved my Portfolio please do check it out
Reubencf/Portfolio
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TonicΒ 
posted an update 15 days ago
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πŸ™‹πŸ»β€β™‚οΈ Hey there folks ,

Turns out : if we predict 🌏 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 πŸ›°οΈ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 πŸ“‘earth-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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Aurelien-MorganΒ 
posted an update 24 days ago
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@retrain-pipelines v0.2.0 is out !
I'm at Station F at My booth with GOSIM Paris 2026 today & tomorrow.
Come meet me for a live in-person demo and a chat !
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Sri-Vigneshwar-DJΒ 
posted an update 27 days ago
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![Feather DB LongMemEval Results]( Hawky-ai/longmemeval-results)

We ran Feather DB v0.8.0 on LongMemEval (ICLR 2025) β€” 500 questions across real multi-session conversations, up to 115K tokens each.

**Score: 0.693** Β· GPT-4o full-context baseline: 0.640
Full 500-question run with Gemini-Flash: **$2.40**

Per-axis breakdown:
β†’ Info-extraction: **0.942**
β†’ Knowledge-update: **0.714**
β†’ Multi-session: **0.606**
β†’ Temporal: **0.477** ← the hard one, Phase 9 addresses this

Architecture: Hybrid BM25+dense Β· adaptive temporal decay Β· embedded (no server) Β· p50 = 0.19ms Β· MIT

pip install feather-db

Raw results + audit JSONs: Hawky-ai/longmemeval-results
TonicΒ 
posted an update about 1 month ago
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πŸ™‹πŸ»β€β™‚οΈ 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 ! πŸš€
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TonicΒ 
posted an update about 1 month ago
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πŸ™‹πŸ»β€β™‚οΈ 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 πŸš€
Aurelien-MorganΒ 
posted an update about 2 months ago
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Launching a workweek of @retrain-pipelines wheels.

Day #1 : Compose
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