ci: migrate test models to pruna-test

tests/engine/test_load.py

@@ -11,7 +11,7 @@
 @pytest.mark.parametrize(
     "model_name, expected_output, should_raise",
     [
-        ("PrunaAI/test-load-tiny-stable-diffusion-pipe-smashed", "PrunaModel", False),
+        ("pruna-test/test-load-tiny-stable-diffusion-pipe-smashed", "PrunaModel", False),
         ("NonExistentRepo/model", None, True),
     ],
 )

tests/engine/test_save.py

@@ -15,14 +15,12 @@
 from pruna.engine.pruna_model import PrunaModel
 
 
-
-@pytest.mark.skipif("HF_TOKEN" not in os.environ, reason="HF_TOKEN environment variable is not set, skipping tests.")
 @pytest.mark.slow
 @pytest.mark.cpu
 def test_save_llm_to_hub() -> None:
     """Test saving an LLM model to the Hugging Face Hub."""
     download_repo_id = "hf-internal-testing/tiny-random-llama4"
-    upload_repo_id = "PrunaAI/test-save-tiny-random-llama4-smashed"
+    upload_repo_id = "pruna-test/test-save-tiny-random-llama4-smashed"
     model = AutoModelForCausalLM.from_pretrained(download_repo_id)
     smash_config = SmashConfig(device="cpu")
     pruna_model = smash(
@@ -31,13 +29,12 @@ def test_save_llm_to_hub() -> None:
     )
     pruna_model.push_to_hub(upload_repo_id, private=False)
 
-@pytest.mark.skipif("HF_TOKEN" not in os.environ, reason="HF_TOKEN environment variable is not set, skipping tests.")
 @pytest.mark.slow
 @pytest.mark.cpu
 def test_save_diffusers_to_hub() -> None:
     """Test saving a diffusers model to the Hugging Face Hub."""
     download_repo_id = "hf-internal-testing/tiny-stable-diffusion-pipe"
-    upload_repo_id = "PrunaAI/test-save-tiny-stable-diffusion-pipe-smashed"
+    upload_repo_id = "pruna-test/test-save-tiny-stable-diffusion-pipe-smashed"
     model = DiffusionPipeline.from_pretrained(download_repo_id)
     smash_config = SmashConfig(device="cpu")
     pruna_model = smash(

tests/fixtures.py

@@ -53,7 +53,7 @@ def dataloader_fixture(request: pytest.FixtureRequest) -> Any:
 
 def whisper_tiny_random_model() -> tuple[Any, SmashConfig]:
     """Whisper tiny random model for speech recognition."""
-    model_id = "PrunaAI/whisper-v3-tiny-random"
+    model_id = "pruna-test/whisper-v3-tiny-random"
     model = pipeline(
         "automatic-speech-recognition",
         model=model_id,
@@ -181,8 +181,8 @@ def get_automodel_image_text_to_text_transformers(model_id: str) -> tuple[Any, S
     ),
     "dummy_lambda": dummy_model,
     # image generation AR models
-    "tiny_janus_pro": partial(get_automodel_image_text_to_text_transformers, "loulou2/tiny_janus"),
-    "wan_tiny_random": partial(get_diffusers_model, "PrunaAI/wan-t2v-tiny-random", torch_dtype=torch.bfloat16),
-    "flux_tiny": partial(get_diffusers_model, "loulou2/tiny_flux", torch_dtype=torch.float16),
-    "tiny_llama": partial(get_automodel_transformers, "loulou2/tiny_llama", torch_dtype=torch.bfloat16),
+    "tiny_janus_pro": partial(get_automodel_image_text_to_text_transformers, "pruna-test/tiny_janus"),
+    "wan_tiny_random": partial(get_diffusers_model, "pruna-test/wan-t2v-tiny-random", torch_dtype=torch.bfloat16),
+    "flux_tiny": partial(get_diffusers_model, "pruna-test/tiny_flux", torch_dtype=torch.float16),
+    "tiny_llama": partial(get_automodel_transformers, "pruna-test/tiny_llama", torch_dtype=torch.bfloat16),
 }