AMD-SHARK-Studio

mirror of https://github.com/nod-ai/AMD-SHARK-Studio.git synced 2026-04-25 03:00:12 -04:00

Files

Chi_Liu f6e9f2d571 Generate Shark Tank for tflite (#142 )

generate shark_tank for tflite

2022-06-20 14:35:35 -07:00

hf_masked_lm

Generate Shark Tank for tflite (#142 )

2022-06-20 14:35:35 -07:00

automodelmaskedlm.py

Add tensorflow models under AutoModelForMaskedLM class.

2022-06-09 22:02:21 +05:30

bert_large_gen.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

bert_large_run.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

bert_large_tf.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

bert_small_gen.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

bert_small_run.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

bert_small_tf_run.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

conftest.py

Add TempFileSaver pytest option to tank TF tests. (#144 )

2022-06-20 10:00:28 -07:00

huggingface_MiniLM_gen.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

huggingface_MiniLM_run.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

huggingface_MiniLM_tf.py

Add seq_classification shark module to run seq_classification

2022-06-14 22:36:19 +05:30

minilm_tf_test.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

README.md

Add seq_classification shark module to run seq_classification

2022-06-14 22:36:19 +05:30

seq_classification.py

Add tf masked lm models (17 models - 12 pass, 5 fail). (#135 )

2022-06-16 07:28:00 -07:00

README.md

Running SharkInference on CPUs, GPUs and MAC.

Run the binary sequence_classification.

The models supported are: hugging face sequence classification

./seq_classification.py --hf_model_name="hf_model" --device="cpu" # Use gpu | vulkan

Once the model is compiled to run on the device mentioned, we can pass in text and get the logits.