by NandhaKishorM
Runs multilingual typed-decision models and provides routing, serving, and integration options
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
On X
Asking everyone to fine tune Just use any kinda agentic harness like claude code, codex etc. Change the code and use free kaggle GPUs to train your own use cases. Base model has limitations
Project: laya→If you are fine tuning laya. Use And use the multi lingual base model
Project: laya→Laya can help protect your website from agentic attack jailbreaking. Imagine a swarm of agents attacking your website, and in under 33 ms, it all gets blocked. Also, before the agent is allowed to call a tool (send email, query a database, execute code, etc.), Laya can answer: “Is this action consistent with the original user request and safe under the current policy?” Low confidence or a clear “no” triggers a human review or hard block. All on your browser with the 421 M model on edge!!!
Project: laya→
A faster, 100% open-source alternative to Jev. It’s called Laya. Both models solve the same problem. You give them unstructured state plus typed questions. Instead of generating a paragraph, they choose from answers you define and return probabilities your code can act on. But they make those decisions differently. Jev is a closed, hosted API. Its architecture and training are not public, but it works zero-shot and supports up to 255 options. Laya is Apache 2.0 licensed, runs locally, and uses open-weight encoder models. Here is how its pipeline works. → It routes by language first. Plain Python selects the English or multilingual checkpoint before inference. → It creates one sequence for each question. Every possible answer gets a `[MASK]` marker. The question and state provide the context needed to compare those options. → It scores instead of generating. A bidirectional encoder and small decision head assign one score to each option. Softmax turns those scores into a probability distribution. → It batches the questions. They run together in one model call, although every question still carries its own copy of the state. This removes network latency and token-by-token generation. In one independent test, Laya took about 35 milliseconds on a local GPU. Jev took roughly 380 milliseconds over the network. Laya also has no per-call API fee, keeps data on your infrastructure, and can run on older GPUs. But it is not a drop-in replacement for every workload. Jev is a hosted generalist that works well out of the box. Laya is a fast base you need to specialize. Laya makes the most sense when you have a stable answer set and representative training data. Fine-tune it for the task, calibrate its probabilities on held-out examples, and send uncertain decisions to a stronger model or human reviewer. Jev gives you stronger zero-shot decisions. Laya gives you speed, privacy, control, and no per-call fee after you teach it your task. The open-source repository is here: If you want to self-host Laya, InstaCloud provides a really nice and easy way to do so. Check this out: If you're new to decision models, I wrote the full breakdown of how Jev. The article is quoted below.
Project: laya→
Laya now supports images, audio, and video along with text, of course. The context window has been increased from 8192 to 73,728 tokens!!. You can attach any LLM as a reader; that means you can attach a 500 M LLM to Gemma 4, Qwen series, GLM series, or trillion-parameter models like Mistral Large 4, Kimi K3(first in the world!), even without much calibration error difference Now you don't need to fine-tune on your dataset; you just fit and calibrate, no gradient descent, and you don't even need a high-end GPU. Includes automated routing, a conformal set, and integrated abstain and outbound(first system decision model). All with an 800-million-parameter model that can even run on your old phone!!! Full blog at: GitHub:
Project: laya→
After one month of launching Laya decision model. The first open-source version of jev and first ever open-source implimentation of RLCD pipeline. Here are the numbers. And Vega U series are gradually disrupting the market for large context multimodal decision model with custom data fit and calibration without retaining
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