Twitxy Saved

urchade

Verified account Fastino Labs

@urchadeDS

Researcher @fastinoAI | Creator of GLiNER Models (GLiNER2, GLiNER2.5, GLiGuard) github.com/urchade
Reunion Island urchade.github.io Joined February 2014
333 Following 826 Followers 3K Posts

Latest posts 5

urchade Verified account @urchadeDS
apolinario (poli) Verified account @multimodalart Oct 22
just updated the Decision Index to 0.4 🧭

what's new?
🛡️ curbing benchmaxxing: score now exclusively made from private & in-house benchmarks
🔒 stronger private benchmarks on new domains
🏆 224 models evaluated, new top models in all sizes

come play 👉huggingface.co/spaces/multimo…
View quoted post on X
urchade Verified account @urchadeDS
Try GLiDE !!!
George Maloney Verified account @george_onx Oct 1
Introducing GLiDE, the first decision model that thinks.

Ranks #1 on the Decision Index, beating Jev by 6.9 points.

GLiDE uses adaptive thinking. To give you an idea of how it works:

It calculates a fast probability distribution.
If the leading answer is uncertain, reasoning is turned on.
Reasoning is then incorporated into the final probabilities.

This means that the model only uses reasoning capabilities for the complex decisions that need them, keeping the model fast while also expanding the types of tasks it can do.

As a result, GLiDE outperforms Jev by 11.5 points in Knowledge and Reasoning and across all five evaluation areas of the Decision Index.

Reasoning allows GLiDE to perform highly complex tasks that traditional decision models struggle with, including:

Choosing among hundreds of tools and possible paths.
Executing, sandboxing, escalating, or blocking sensitive operations.
Checking outputs involving arithmetic, code, dates, or multiple facts.

GLiDE is available now through the Fastino API. Use code USE_GLIDE for free credits.

GLiDE API: agent.fastino.ai
View quoted post on X
1 replies 1 reposts 12 likes 552 views View on X
urchade Verified account @urchadeDS
GLiNER2.5-Decide on device ⚡
GLiNER2.5-Decide (DeBERTa-v3-large, 340M) converted to LiteRT: fp16 weights, answers identical to the official fp32 model on 361/361 test requests, 66–70 ms per request on a Galaxy S26 GPU at 128 tokens, 175 ms at 256. Card, Python and Android sample: huggingface.co/litert-communi…
View quoted post on X