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You can run Laya locally as a command-line tool, call its Apple Silicon MLX runtime directly from Python, or expose it as an HTTP service for a Jev-style client. Laya is a separate open-weight model, not the official Jev model running offline. Local inference avoids sending each prediction to Jev’s hosted endpoint, but downloading the model and dependencies may still require internet access.
Choose the local execution mode that fits your setup
| Mode | Best fit | What it does |
|---|---|---|
| CLI | Trying Laya or running occasional decisions from a terminal | Runs routing commands and, with prediction enabled, local model inference. |
| Python with MLX | Integrating inference into a Python application on Apple Silicon | Loads a model in-process; no HTTP server is required. |
| HTTP service | Keeping an existing Jev-style HTTP integration | Serves a local endpoint that accepts typed-decision requests. |
These installation commands and interfaces are documented by the Laya project repository and the Jev local alternatives page. Package names and options can change, so check those primary docs for the current release before deploying.
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Run a quick trial from the command line
Install the package, then use laya with the text or state you want processed:
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laya "Your input here"
The repository documents this basic invocation for routing. Add --predict to perform model inference:
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laya "Your input here" --predictIt also documents an interactive mode. Routing can run without downloading a checkpoint; prediction requires a checkpoint, and the first download requires network access. Subsequent inference can run locally once the model is available.
Call Laya from Python on Apple Silicon
The MLX option runs inference in the Python process rather than behind a server. The official local alternatives page documents this setup:
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python3.11 -m venv .venv
source .venv/bin/activate
pip install laya-mlxLoad the documented model and pass a state plus typed questions to predict:
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from laya_mlx import LayaAgent
agent = LayaAgent("aac6fef/laya-mlx")
answers = agent.predict(
state="The input state or context",
questions=[
{"name": "category", "type": "choice", "options": ["yes", "no"]}
],
)
print(answers)The page identifies choice, score, and noul as supported decision types, and points to a separate multilingual MLX model. Follow the current model page for exact constructor and question-schema details, which may differ by package version.
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Serve a local endpoint for a Jev-style client
If your application already sends HTTP requests in the Jev-style protocol, the repository documents an optional serving extra and a local server command:
pip install "laya[serve]"
LAYA_DEVICE=cuda LAYA_PRELOAD=1 laya-serveThe service exposes POST /v1/systemone. Send a state and typed questions, then process the returned answers and usage information. The repository describes the protocol as Jev-compatible, but compatibility of the request shape does not mean the same model or results.
The server documentation also describes configuration for host, port, device, model list, preload behavior, thread limit, and optional bearer-token authentication. Verify current defaults and environment-variable names in the repository before copying a configuration into production. If you bind the service to an interface reachable beyond the local machine, restrict access and configure authentication as appropriate.
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Understand what changes when you replace Jev
Laya is a separate open-weight model with a typed interface resembling Jev’s; it is not official Jev weights packaged for offline use. The Laya deployment page describes it as an independent local counterpart and advises validating model claims against your own data before relying on a threshold.
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The documented typed outputs include choice, score, and noul (yes/no). Exact schema options can vary across runtimes, so do not assume that CLI, MLX, and HTTP serving expose every setting identically. Likewise, an API-compatible client may need configuration changes for the local base URL, authentication, or supported request details.
Test quality before using decisions in production
Neither project benchmarks nor a single outside comparison establish how Laya will perform on your labels, prompts, languages, or decision thresholds. The repository’s comparison notes that Jev figures came from third parties and that sample sizes and prompts differ. A separate BKS-Lab comparison published on 24 September 2026 reports results across 1,189 cases and finds that outcomes vary by decision type; that is one test, not a guarantee for other workloads. See the repository’s benchmark notes and BKS-Lab comparison for their specific methods and caveats.
Before routing consequential actions through a local model, build a held-out evaluation set representative of your actual inputs and label distribution. Measure task accuracy and calibration for the specific decision types and thresholds you intend to use, and test how the application handles malformed, missing, or low-confidence results. These checks are practical deployment guidance, not claims that either cited benchmark tested your application.
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With local inference, prediction can happen on your own machine or server rather than sending every input to Jev’s hosted endpoint. That does not make setup automatically offline: package installation and the initial checkpoint retrieval may contact external services. For an offline deployment, acquire the required packages and model artifacts in advance and verify that runtime behavior does not attempt additional downloads.
Choose the CLI for a simple trial, MLX for an in-process Apple Silicon integration, or the HTTP service when retaining a Jev-style client matters most. In every mode, validate the model on the intended task rather than treating interface similarity as evidence of equivalent accuracy.
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