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Use tiny-random-minicpmv-2_6 #1000

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merged 7 commits into from
Oct 21, 2024
Merged

Use tiny-random-minicpmv-2_6 #1000

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115336b
Use tiny-random-minicpmv-2_6
Wovchena Oct 17, 2024
9774e75
Merge branch 'master' into use-tiny-random-minicpmv-2_6
andrei-kochin Oct 17, 2024
18c267f
Merge branch 'master' into use-tiny-random-minicpmv-2_6
Wovchena Oct 17, 2024
daf0146
Merge branch 'master' into use-tiny-random-minicpmv-2_6
ilya-lavrenov Oct 17, 2024
1a4a24a
Merge branch 'master' into use-tiny-random-minicpmv-2_6
Wovchena Oct 18, 2024
862978d
Merge branch 'master' into use-tiny-random-minicpmv-2_6
Wovchena Oct 21, 2024
e18be28
Add processor saving
Wovchena Oct 21, 2024
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8 changes: 4 additions & 4 deletions tests/python_tests/test_vlm_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
def get_ov_model(model_dir):
if (model_dir / "openvino_language_model.xml").exists():
return model_dir
model_id = "openbmb/MiniCPM-V-2_6"
model_id = "katuni4ka/tiny-random-minicpmv-2_6"
processor = transformers.AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
processor.tokenizer.save_pretrained(model_dir)
ov_tokenizer, ov_detokenizer = openvino_tokenizers.convert_tokenizer(processor.tokenizer, with_detokenizer=True)
Expand Down Expand Up @@ -48,7 +48,7 @@ def test_vlm_pipeline(cache):
def streamer(word: str) -> bool:
return False

model_path = get_ov_model(cache.mkdir("MiniCPM-V-2_6"))
model_path = get_ov_model(cache.mkdir("tiny-random-minicpmv-2_6"))

for links in image_links_for_testing:
images = []
Expand All @@ -69,7 +69,7 @@ def streamer(word: str) -> bool:
@pytest.mark.precommit
@pytest.mark.nightly
def test_vlm_get_tokenizer(cache):
model_path = get_ov_model(cache.mkdir("MiniCPM-V-2_6"))
model_path = get_ov_model(cache.mkdir("tiny-random-minicpmv-2_6"))
pipe = VLMPipeline(str(model_path), "CPU")
tokenizer = pipe.get_tokenizer()
tokenizer.encode("")
Expand All @@ -83,7 +83,7 @@ def test_vlm_get_tokenizer(cache):
])
@pytest.mark.skip("Enable after sampler are enabled")
def test_sampling(config, cache):
model_path = get_ov_model(cache.mkdir("MiniCPM-V-2_6"))
model_path = get_ov_model(cache.mkdir("tiny-random-minicpmv-2_6"))
image = get_image_by_link(image_links[0])
pipe = VLMPipeline(str(model_path), "CPU")
pipe.generate(prompts[0], image=image, generation_config=config)
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