Spaces:
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Clean up optimization status metadata
Browse files
README.md
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@@ -62,8 +62,9 @@ ZeroGPU runtime.
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- Repeated runs with the same selected model reuse the loaded model inside the
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ZeroGPU worker when the worker stays warm. Run metadata includes `cache_hit`
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and `load_elapsed_s` so this is visible.
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- Successful gated-repo access checks are cached briefly
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repo ID to avoid a Hugging Face `HEAD` request on every
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- The `stable-audio-3-optimized` repo currently provides MLX, ONNX, and
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TensorRT assets. This Space keeps the portable PyTorch path because the
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TensorRT engines are prebuilt for `sm_90`, while the current ZeroGPU host is
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- Repeated runs with the same selected model reuse the loaded model inside the
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ZeroGPU worker when the worker stays warm. Run metadata includes `cache_hit`
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and `load_elapsed_s` so this is visible.
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+
- Successful gated-repo access checks are cached briefly inside the worker per
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token digest and repo ID to avoid a Hugging Face `HEAD` request on every
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+
generation.
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- The `stable-audio-3-optimized` repo currently provides MLX, ONNX, and
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TensorRT assets. This Space keeps the portable PyTorch path because the
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TensorRT engines are prebuilt for `sm_90`, while the current ZeroGPU host is
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app.py
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@@ -737,7 +737,6 @@ def runtime_status(
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"hf_api_token_present": bool(hf_api_token_value(hf_api_token)),
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"loaded_generation_model": MODEL_CACHE["key"],
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"loaded_autoencoder": AE_CACHE["key"],
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"access_cache_entries": len(ACCESS_CACHE),
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}
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"hf_api_token_present": bool(hf_api_token_value(hf_api_token)),
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"loaded_generation_model": MODEL_CACHE["key"],
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"loaded_autoencoder": AE_CACHE["key"],
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}
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