Update model card: remove per-run selection counts and evaluation-set accuracy claims

README.md

@@ -29,29 +29,50 @@ Released artifact: **top-3 FP64 parameter average**. Decoded with **beam 5, leng
 | `model.pt` | 17,522,653,578 |
 | `omniASR_tokenizer_written_v2.model` | 91,481 |
 | `card.yaml` | — |
+| `requirements.txt` | — |
 
 Total weights: 17,522,653,578 bytes (17.52 GB).
 
 ## Role in the pipeline
 
-Member of the 4-way conservative word ROVER on the Lingala lane.
+Member of the Lingala TTIA fusion.
 
-This model is **one member of an ensemble** and is not intended to be used alone. The full
-routing, decoding, fusion and post-processing pipeline — and the scores it produces — are in
-the [solution repository](https://github.com/DariusTheGeek/waxal-asr-solution).
+This model is **one component of an ensemble solution** and is not intended to be used
+alone. The routing, decoding, fusion and post-processing pipeline are in the
+[solution repository](https://github.com/DariusTheGeek/waxal-asr-solution).
+
+## How to use
+
+This is a fairseq2 / `omnilingual-asr` checkpoint, loaded through the asset
+card shipped alongside it (`card.yaml`). The solution repository wraps the whole
+contract -- pinned environment, batch-size-1 decode, tokenizer wiring -- in one
+CLI:
+
+```bash
+git clone https://github.com/DariusTheGeek/waxal-asr-solution
+cd waxal-asr-solution && bash install.sh        # pinned environments, ~15 min
+python models/download_models.py --repo waxal-lin-omniasr-llm-3b
+
+.venvs/omni/bin/python inference/decode/omniasr.py \
+    --config configs/lin/llm3b.yaml \
+    --audio path/to/wav_dir --output transcripts.csv
+```
+
+`--weights` accepts any directory holding this repo's files, e.g. the path
+returned by `huggingface_hub.snapshot_download("DariusTheGeek/waxal-lin-omniasr-llm-3b")`.
+`requirements.txt` in this repo pins the runtime alone; the environment lock
+the release was verified under is `env/requirements-omni.txt` in the solution
+repository.
 
 ## Provenance
 
 | | |
 |---|---|
 | Parent | `facebook/omniASR-LLM-3B-v2` |
-| Internal run ID | `X0038` |
 | Language | Lingala |
 | Fine-tuning data | Waxal Lingala/Shona supervised split ([`google/WaxalNLP`](https://huggingface.co/datasets/google/WaxalNLP)) |
 | Seed | 42 |
 
-No evaluation-set label was used at any point in training or model selection.
-
 ## Usage
 
 See [`https://github.com/DariusTheGeek/waxal-asr-solution`](https://github.com/DariusTheGeek/waxal-asr-solution) for the environment locks, decode configuration and the exact