Document PyPI ERABI 0.1.2 availability

README.md

@@ -41,7 +41,7 @@ python -m pip install erabi
 erabi predict --request request.json
 ```
 
-The repository contains three inference formats from the same checkpoint: `model.safetensors` (PyTorch), `onnx/fp32/model.onnx` (CPU), and `onnx/fp16/model.onnx` (NVIDIA GPU). ERABI 0.1.2 or newer is required for `--model-format`; 0.1.1 uses PyTorch safetensors. Until 0.1.2 is published on PyPI, install the [GitHub source](https://github.com/sugarkwork/erabi) with `python -m pip install --upgrade "git+https://github.com/sugarkwork/erabi.git"`. `--model-format auto` downloads only the selected variant: FP32 ONNX for CPU with ONNX Runtime, FP16 ONNX for CUDA with CUDA Execution Provider, and otherwise PyTorch safetensors. Install the compatible `onnxruntime` (CPU) or `onnxruntime-gpu` (GPU) separately; do not install both in one environment. You can also select `--model-format pytorch`, `onnx-fp32`, or `onnx-fp16` explicitly.
+The repository contains three inference formats from the same checkpoint: `model.safetensors` (PyTorch), `onnx/fp32/model.onnx` (CPU), and `onnx/fp16/model.onnx` (NVIDIA GPU). ERABI 0.1.2 or newer is required for `--model-format`; 0.1.1 uses PyTorch safetensors. Upgrade with `python -m pip install --upgrade erabi`. `--model-format auto` downloads only the selected variant: FP32 ONNX for CPU with ONNX Runtime, FP16 ONNX for CUDA with CUDA Execution Provider, and otherwise PyTorch safetensors. Install the compatible `onnxruntime` (CPU) or `onnxruntime-gpu` (GPU) separately; do not install both in one environment. You can also select `--model-format pytorch`, `onnx-fp32`, or `onnx-fp16` explicitly.
 
 The ONNX variants preserved the top-ranked choice on 90/90 unreviewed synthetic comparison cases; this is not independent human-gold validation. Experimental INT8 variants changed predictions substantially and are not distributed. The first invocation downloads the selected model; later invocations use the Hugging Face cache. Input and output JSON contracts and runtime recommendations are documented in the [ERABI README](https://github.com/sugarkwork/erabi#モデル形式の自動選択とおすすめ). The input limit is 512 tokens; overlong inputs are rejected rather than silently truncated. The candidate probabilities are not calibrated confidence guarantees.