docs: sync model card with v0.1.0 release

README.md

@@ -19,11 +19,10 @@ license: mit
 # Premove ITN v0.1.0
 
 Premove ITN is an open-weight contextual inverse text normalization system for
-English voice-agent transcripts. It turns spoken-form ASR text into structured
+English voice-agent transcripts. It is the contextual scoring model used by the
+`premove-itn` Python package. It turns spoken-form ASR text into structured
 written text:
 
-**GitHub repository:** [`premove-ai/premove-itn`](https://github.com/premove-ai/premove-itn)
-
 ```text
 call me at four thirty  →  call me at 04:30
 the total is twenty dollars  →  the total is $20
@@ -51,15 +50,16 @@ print(itn.normalize("call me at four thirty"))
 # call me at 04:30
 ```
 
-The `premove-itn` PyPI release is not published yet. Until the public package
-release, contributors can build and install the release wheel from the GitHub
-repository. Create one `PremoveITN` instance and reuse it; model initialization
-is expensive compared with warm normalization.
+Install the latest package with `pip install premove-itn`, or pin the current
+release with `pip install premove-itn==0.1.0`. Create one
+`PremoveITN` instance and reuse it; model initialization is expensive compared
+with warm normalization.
 
 `device="auto"` selects CUDA when available, then Apple MPS, then CPU. The
-current release candidate has been validated end-to-end only on macOS Apple
-Silicon with Python 3.11 and MPS. Other environments require release
-validation.
+Release wheels are validated on macOS 14+ arm64 and `manylinux_2_28` x86_64
+for Python 3.11–3.13. Real frozen-model inference is validated on Apple
+Silicon MPS and Linux CPU. Other environments require release validation. The source repository retains the current
+[platform support matrix](https://github.com/premove-ai/premove-itn/blob/main/docs/platform-support.md).
 
 ## Architecture
 
@@ -105,13 +105,13 @@ match separately measures the complete canonical output.
 | Thutmose | 59.39% | 22.13% | 15.98 ms |
 | text-processing-rs | 55.79% | 16.53% | 0.14 ms |
 
-Premove led measured semantic accuracy overall and on the 400 dedicated
+Premove ITN led measured semantic accuracy overall and on the 400 dedicated
 voice-agent rows. It did not lead latency.
 
 Latency used sequential batch-one requests on an Apple M4 MacBook Air with
 MPS, an optimized Rust extension, and eight Rayon workers. Models were loaded
 and warmed before request latency was measured. Download and initialization
-are excluded. The Premove timing used the release Rust extension. The backend
+are excluded. The Premove ITN timing used the release Rust extension. The backend
 evaluation was blind: each backend received only transcript text.
 Independent human gold adjudication is a separate task and remains pending.
 
@@ -122,7 +122,7 @@ and
 
 ## Intended use
 
-Use Premove for English voice-agent transcripts in which numbers, dates,
+Use Premove ITN for English voice-agent transcripts in which numbers, dates,
 times, money, phone values, identifiers, URLs, and related structured values
 need sentence-level disambiguation. The runtime receives only transcript text.
 Candidate metadata is generated internally.
@@ -144,10 +144,10 @@ Candidate metadata is generated internally.
 - The benchmark is synthetic and does not measure live production traffic.
 - Weaker measured categories include URL, MONEY, CARDINAL, TIME,
   REFERENCE_ID, and VERSION.
-- Independent human gold adjudication and broader contamination checks remain
-  incomplete. The backend evaluation itself was blind.
-- End-to-end release validation currently covers macOS Apple Silicon, Python
-  3.11, and MPS only.
+- Independent human gold adjudication and broader contamination checks are
+  outside the v0.1.0 release gate. The backend evaluation itself was blind.
+- CUDA, Windows, macOS Intel, Linux ARM64, and other accelerators are not
+  validated v0.1.0 support claims.
 
 ## Release identity and provenance