urchade @urchadeDS
Researcher @fastinoAI | Creator of GLiNER Models (GLiNER2, GLiNER2.5, GLiGuard) https://t.co/pajN3oi7ZO urchade.github.io Reunion Island Joined February 2014-
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Another model lined up. This one’s a little different.
Another model lined up. This one’s a little different.
Decision models are gaining traction, but it's not always obvious where they fit into your applications. Last week, we released GLiNER2.5-Decide, our 340M-parameter decision model built for fast, structured classification. Models like these can fit into a lot of different parts of your workflow, so to help you get started, we’ve put together a guide to 7 practical use cases for GLiNER2.5-Decide, from intent routing to tool calling, guardrails, browser use, and model-as-a-judge. Swipe through for ideas on how you can use it in production. GLiNER2.5-Decide is available for inference and fine-tuning through the Fastino API. Start building: agent.fastino.ai
GLiNER 2.5 Multi from @fastinoAI using CoreAI running on an iPhone 17 Pro
Classification is only one step in the decision pipeline. GLiNER2.5-Decide pairs classification with information extraction, so each decision draws on the relevant context in the input. At 340M parameters, our model handles typed decisions, extraction, relations, and structured records while matching the performance of LLMs at a fraction of the cost and latency. For those who want the benefits of a compact, flexible model without the work of managing it locally, GLiNER2.5-Decide is available for inference and training through the Fastino API. Access the API via the Fastino platform: agent.fastino.ai/auth
an open model matched Jev on two clinical tasks. i haven't fine-tuned a single weight 🔥 @fastinoAI's GLiNER2.5-Decide (340M), zero-shot on a CPU: 12/12 on assertion status and allergy documentation, like Jev. full set: 36/48 vs 48/48. who's fine-tuning it on clinical notes?
Be ready for a week of zero shot performance GAINS for GLiNER 💪
GLiNER2.5-Decide is bomb 💣 Runs incredibly fast (on my mac), and did much better than Jev (on Fastino evals) It'd be very interesting to see the System-1 domain evolve, especially with OSS models and evals !
Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning
Your decision on model selection boils down to whether you care about data privacy and if you truly need the model fast enough to sit in the critical path (<50ms). Less than an hour on the Fastino training api (familiar if you have ever used Tinker) will get you a model that beats Jev on your data and weights you can download. But something we are launching this week will clear all three bars, zero shot. Super excited to show you all.
Ok, this seems great, especially at that size. Small enough for game AI probably (in conjunction with conventional game AI)
Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning
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…
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…
An ad blocker 🛑 where the AI 🧠 runs inside your browser. No server. No API key. No block list. @fastinoAI's GLiNER2.5-Decide runs on your GPU with WebGPU, reads each part of the page and decides "ad or not?" in milliseconds ⚡️ Your browsing never leaves the tab. 🔐 github.com/shreyaskarnik/…
We have converted GLiNER2.5-Decide to coreml. ~4× faster, ~5× less Peak RAM, half the size. model: huggingface.co/FluidInference code: github.com/FluidInference
Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning
Fine-tuning can allow compact models to match or outperform models 23–90x larger. A few months ago, @urchadeDS and @m_newhaus post-trained GLiNER2 to create GLiGuard, a 300M-parameter guardrail model designed for content moderation and safety classification. Next month, we’ll be presenting the research paper for GLiGuard at COLM in San Francisco. Comparable open-source SOTA guardrail models typically range from 7B to 27B parameters. At just 300M, GLiGuard maintains comparable accuracy while running 16x faster. The key was combining an architecture well suited to classifying harm categories with high-quality synthetic training data. GLiGuard is open weight and available to download on @huggingface. You can also check out our most recent release, GLiNER2.5-Decide, for other schema-defined decision-making tasks. - GLiGuard: huggingface.co/fastino/gligua… - GLiNER2.5-Decide: huggingface.co/fastino/GLiNER…
The Hub can be a firehose of new releases. I used @fastinoAI's GLiNER2.5-Decide to build a Space that tags every new model, dataset and Daily Paper on @huggingface into 48 topics, usually a minute or two after it changes. Runs on one CPU, no LLM. huggingface.co/spaces/davanst…
A lot of new demos for decision models have been circulating over the last two weeks. Decision models have encouraged people to experiment with use cases beyond just parsing text, which inspired our team to build a few demos of our own. See what our team has been able to create with GLiNER: fastino.ai/models/demo-li…
Codex + GLiNER = download your own decision model that runs on your Macbook
A lot of new demos for decision models have been circulating over the last two weeks. Decision models have encouraged people to experiment with use cases beyond just parsing text, which inspired our team to build a few demos of our own. See what our team has been able to create
@DhruvAtreja1 kept loosing his dough divider - so fine tuned GLiNER and put it in a robot to find it for him Try making something yourself at agent.fastino.ai
You can fine-tune your OWN Jev for <$10 on your OWN data. Just give your claude/codex this skill docs.fastino.ai/concepts/gline… Here is a finetuned Gliner doing robotic navigation
⬇️⬇️⬇️
You can fine-tune your OWN Jev for <$10 on your OWN data. Just give your claude/codex this skill docs.fastino.ai/concepts/gline… Here is a finetuned Gliner doing robotic navigation
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