From 8a91121f08970f2450c96338ee481de8a161aec5 Mon Sep 17 00:00:00 2001 From: jason Date: Mon, 2 Nov 2026 10:31:50 +1100 Subject: [PATCH] Clarify local installation, training sources and live service status --- CHANGELOG.md | 4 ++++ README.md | 14 +++++++++++++- jev_style/__init__.py | 2 +- server.json | 4 ++-- 4 files changed, 20 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 473875e..22d6753 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,10 @@ Versions follow [semantic versioning](https://semver.org/). Each release pins the model revisions it loads (`jev_style/models.py`), so upgrading the package is what changes the weights you get. +## 0.6.3 (2026-11-02) + +Documentation-only release: clarify the official local installation, separate local inference from hosted API and third-party Jev services, place the 2B v4 teacher-label provenance alongside the independence statement, and link the dated/live service-status source and reproducible routing comparison. PyPI now serves the same corrected README as the repository. Model revisions, dependencies, inference behavior and weights are unchanged from 0.6.2. + ## 0.6.2 (2026-10-29) - Synchronize the PyPI description with the project’s local classification, routing, diff --git a/README.md b/README.md index a2dcb68..8ea8cb2 100644 --- a/README.md +++ b/README.md @@ -32,6 +32,14 @@ Give the model text or JSON, optional images, and typed questions. It returns ye ![The Playground answering a support-ticket request, then the agent-approval demo allowing, asking about and denying tool calls](https://raw.githubusercontent.com/lawrence3699/jev-style/main/docs/demo.gif) +## Identity, training sources and service status + +JevStyle is maintained by **Chaoliang Yan**. **The 2B v4 model was trained in part on soft labels distilled from TypeSafe Jev 1.14 through its public API.** JevStyle is an independent project with no TypeSafe affiliation or endorsement. The package does not bundle raw teacher-response datasets; that packaging detail does not negate the use of teacher outputs in training. Training sources differ by release; consult each model card. + +**Local inference runs the downloaded JevStyle weights on your machine and needs no hosted API key.** Install the `jev-style` Python package below. A third-party Jev skill that simulates decisions with an agent or requires TypeSafe/OpenRouter credentials is a different execution path. Follow the [official local installation guide](https://jevstyle.com/guides/run-locally/) for text, images and backend selection. + +**Hosted service status is separate from local inference.** Check the [service status and current limits](https://jevstyle.com/api/#service-status) or [live health endpoint](https://api.jevstyle.com/healthz) when you need the hosted API. A health check is a point-in-time readiness signal, not an end-to-end inference guarantee. The hosted API, Hugging Face demos and local server are separate services. + ## Quick Start ### Try it in the browser @@ -40,7 +48,7 @@ The [2B Space](https://huggingface.co/spaces/chaoliangUNSW/jev-style-2b) runs Je ### Run it locally -You'll need Python 3.10 or newer. Install **one** backend for your machine: +You'll need Python 3.10 or newer. Create and activate a virtual environment, then install **one** backend for your machine. No TypeSafe or OpenRouter account, key or agent simulation is needed for this local setup. ```bash # Apple silicon: local text decisions @@ -81,6 +89,10 @@ On an NVIDIA GPU, `jev-style serve --release 2b --backend torch --dtype bfloat16 To install the command line tool on its own, use [uv](https://docs.astral.sh/uv/): `uv tool install "jev-style[all]"` on Apple silicon, `uv tool install "jev-style[torch]"` elsewhere. The MCP server is part of every install since 0.3.0 (`jev-style[mcp]` still works). +### Check the local result and choose a fallback + +Use the [reproducible six-intent BANKING77 comparison](https://jevstyle.com/benchmarks/banking-routing/) as an example of evidence, not a ranking: on its fixed 240 test requests, TF–IDF + logistic regression got 229 correct and JevStyle 0.8B got 224. The development-selected JevStyle acceptance rule exceeded the target error budget on the final test set. The page publishes all predictions, versions and a reproduction command. Measure your own labels, calibration, accepted-request errors and complete latency before replacing an existing route. + ## Recipes Clone this repository to run the [three complete examples](https://github.com/lawrence3699/jev-style/tree/main/examples/recipes). Each prints JSON and makes no changes to tickets or payments. diff --git a/jev_style/__init__.py b/jev_style/__init__.py index cba6e23..2a2846a 100644 --- a/jev_style/__init__.py +++ b/jev_style/__init__.py @@ -1,5 +1,5 @@ """Jev-Style: small, calibrated, local decision models with a systemone-compatible API.""" -__version__ = "0.6.2" +__version__ = "0.6.3" from .client import JevStyle, JevStyleError, choice, classify, configure, decide, image, noul, score # noqa: E402 diff --git a/server.json b/server.json index c1a57ac..8bb6912 100644 --- a/server.json +++ b/server.json @@ -8,13 +8,13 @@ "source": "github" }, "websiteUrl": "https://jevstyle.com", - "version": "0.6.2", + "version": "0.6.3", "packages": [ { "registryType": "pypi", "registryBaseUrl": "https://pypi.org", "identifier": "jev-style", - "version": "0.6.2", + "version": "0.6.3", "runtimeHint": "uvx", "transport": { "type": "stdio"