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We now have a native integration with @opencode! And implementing it couldn’t be easier. Just select Pioneer as your provider in the UI and you’re all set.
New native integration to use Pioneer with @opencode is here 👾
Fastino has signed the Microsoft letter 🫡
@fastinoAI has always believed the future of AI will run on open-source, specialized models, rather than closed models controlled by a few. We've co-signed this letter, alongside many others, in support of continued access to open-weight models and the implementation of safety
@fastinoAI has always believed the future of AI will run on open-source, specialized models, rather than closed models controlled by a few. We've co-signed this letter, alongside many others, in support of continued access to open-weight models and the implementation of safety guardrails over restrictions.
At @fastinoAI, we've been building open-weight models since day one, so this initiative hits particularly close to home. That’s why we're co-signing this letter in support of keeping open-weight models accessible. x.com/george_onx/sta…
@fastinoAI has always believed the future of AI will run on open-source, specialized models, rather than closed models controlled by a few. We've co-signed this letter, alongside many others, in support of continued access to open-weight models and the implementation of safety
Build an agent that improves itself and compete for $40k+ in prizes. We’re excited to be partnering with @tokensandai along with teams at @GoogleDeepMind, @GuildAI, @replayio, @GoSenso, @band_hq, @ActianCorp, and @dg717sf to co-host the Self-Evolving Agents Hackathon. Throughout the event, you'll get direct access to experts and hands-on time with some of the top tools in the agent ecosystem. Spots are limited. Register now: luma.com/swarmhack
Big news: our researcher @urchadeDS's paper on GLiGuard has been accepted to COLM (Conference on Language Modeling) 2026 📄 The paper discusses how GLiGuard, a 0.3B encoder-based SLM, approaches safety moderation as a text classification problem rather than text generation one, allowing it to perform four safety moderation tasks in a single forward pass. Congratulations, Urchade! 🎉 Links to the full paper and model page on @huggingface below.
🚨🚨 Didier Deschamps 🇫🇷 : « Je pose une question et je ne vais pas y répondre, EST-CE QUE L’ARBITRE A LE NIVEAU POUR ARBITRER UNE DEMI-FINALE DE COUPE DU MONDE ? » 😱 🎙️ @M6
GLiNER2 just passed 1M monthly downloads, 2x in two months. Firing a frontier model at every step is slow and expensive, when most steps (extraction, classification, routing) are exactly what a small, deterministic model does best. Across the entire family, GLiNER has now passed 40 million total downloads. All open source. We’ve released three new models in the last month and will keep contributing to the open source community. Link to our @huggingface: huggingface.co/fastino
AJ DYBANTSA vs DARRYN PETERSON: 27 POINTS 24 POINTS 7 REBOUNDS 8 TURNOVERS 2 ASSISTS 9 FOULS 7/18 FG 6/18 FG WIZARDS BEAT JAZZ 92-88 AJ DYBANTSA WON THE BATTLE 😤
We combined our two most popular models, launching our most powerful open source model yet for both LLM guardrails and PII extraction. GLiNER2-Guardrails-PII-Multi is a 0.3B open weights model with combined safety moderation and PII filtering. We trained it without touching any real-world PII data. This was hardest hard part, because PII is sensitive by nature, so real training data is scarce, narrow, overused, etc. We used the Pioneer API to generate our multi-task, multilingual dataset: > WildGuardTrain + jailbreak strategy > 42 PII entity types generated via constraint-driven framework Model weights are on @huggingface, as always. You can also try it on Pioneer. 🤗: huggingface.co/fastino/GLiNER… 🔗: pioneer.ai x.com/fastinoAI/stat…
Our newest open source model is a guardrail + PII combination! A quick overview: > 0.3B parameters > 4 guardrail tasks (classification) + PII extraction in a > single forward pass > Performance matches the individual models > 🤗 Weights are on @huggingface. Huge shoutout to @urchadeDS on this one!
@LoganMarkewich Hey, have you tried small versions of gliner ? (e.g. gliner_small-v2.5) What throughput target are you aiming for?
Huge congrats to @doctolib on releasing `finemed-entity-extractor`! It’s built on GLiNER2 and extracts 8 medical entity types from French text. They also released: > FineMed → a large French medical pretraining corpus > DoctoBERT → a SOTA French medical encoder We love to see the community build on top of GLiNER2 models, especially in domains like medicine which can be so impactful! 🤗: huggingface.co/doctolib-lab/f… 📄: arxiv.org/html/2606.2207…
@nikhilnayak268 @fastinoAI great @nikhilnayak268 👨🍳
@Pranav2278 @ash_csx Maybe a new architecture called GLiTab (cc @ash_csx 👀)
First model is now open-source: x.com/ash_csx/status…
As promised, we trained an open guardrail model with accuracy matching huge decoder models. Free inference on Pioneer! It’s 16x faster and up to 93x smaller than current SOTA models. It handles multiple tasks, including prompt injection, jailbreak attempts, response harmfulness
SOTA accuracy at 300M parameters, this one was fun to build. @urchadeDS @ash_csx @fastinoAI
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