Instructions to use FluidInference/laya-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use FluidInference/laya-coreml with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download reports/benchmark-jev-coreml.json from FluidInference/laya-coreml: direct link, hf CLI and curl.
- Browser
- Download file 2.54 kB
-
https://huggingface.co/FluidInference/laya-coreml/resolve/main/reports/benchmark-jev-coreml.json
- Command line
-
hf download hf://FluidInference/laya-coreml/reports/benchmark-jev-coreml.json
-
curl -L -o benchmark-jev-coreml.json https://huggingface.co/FluidInference/laya-coreml/resolve/main/reports/benchmark-jev-coreml.json
2.54 kB
| { | |
| "chip" : "Apple M5 Pro", | |
| "elapsed_s" : 2.7073659896850586, | |
| "latency_ms" : { | |
| "p50" : 4.4866669999999997, | |
| "p95" : 9.6365829999999999 | |
| }, | |
| "lengths" : [ | |
| 128, | |
| 256, | |
| 512, | |
| 1024 | |
| ], | |
| "load_s" : 2.0176939964294434, | |
| "questions" : 500, | |
| "suites" : { | |
| "emotion" : { | |
| "accuracy" : 0.57999999999999996, | |
| "buckets" : { | |
| "L128" : 100 | |
| }, | |
| "dropped" : 0, | |
| "latency_ms" : { | |
| "p50" : 4.0849580000000003, | |
| "p95" : 4.5919999999999996 | |
| }, | |
| "max_probability_delta_vs_reference" : 0.014793992042541504, | |
| "n" : 100, | |
| "reference_argmax_agreement" : 1, | |
| "reference_compared" : 100, | |
| "state_truncated" : 0 | |
| }, | |
| "news_topic" : { | |
| "accuracy" : 0.96999999999999997, | |
| "buckets" : { | |
| "L128" : 86, | |
| "L256" : 14 | |
| }, | |
| "dropped" : 0, | |
| "latency_ms" : { | |
| "p50" : 5.5224159999999998, | |
| "p95" : 9.2874169999999996 | |
| }, | |
| "max_probability_delta_vs_reference" : 0.010490596294403076, | |
| "n" : 100, | |
| "reference_argmax_agreement" : 1, | |
| "reference_compared" : 100, | |
| "state_truncated" : 0 | |
| }, | |
| "prompt_injection" : { | |
| "accuracy" : 0.65000000000000002, | |
| "buckets" : { | |
| "L128" : 96, | |
| "L256" : 4 | |
| }, | |
| "dropped" : 0, | |
| "latency_ms" : { | |
| "p50" : 4.3983749999999997, | |
| "p95" : 6.5922919999999996 | |
| }, | |
| "max_probability_delta_vs_reference" : 0.02795100212097168, | |
| "n" : 100, | |
| "reference_argmax_agreement" : 0.98999999999999999, | |
| "reference_compared" : 100, | |
| "state_truncated" : 0 | |
| }, | |
| "review_stars" : { | |
| "accuracy" : 0.34999999999999998, | |
| "buckets" : { | |
| "L128" : 28, | |
| "L256" : 35, | |
| "L512" : 26, | |
| "L1024" : 11 | |
| }, | |
| "dropped" : 0, | |
| "latency_ms" : { | |
| "p50" : 5.9012919999999998, | |
| "p95" : 18.642083 | |
| }, | |
| "max_probability_delta_vs_reference" : 0.0017490386962890625, | |
| "n" : 100, | |
| "reference_argmax_agreement" : 1, | |
| "reference_compared" : 100, | |
| "state_truncated" : 0 | |
| }, | |
| "sms_spam" : { | |
| "accuracy" : 0.57999999999999996, | |
| "buckets" : { | |
| "L128" : 100 | |
| }, | |
| "dropped" : 0, | |
| "latency_ms" : { | |
| "p50" : 4.1685420000000004, | |
| "p95" : 5.0857080000000003 | |
| }, | |
| "max_probability_delta_vs_reference" : 0.020002603530883789, | |
| "n" : 100, | |
| "reference_argmax_agreement" : 1, | |
| "reference_compared" : 100, | |
| "state_truncated" : 0 | |
| } | |
| } | |
| } |