Update README.md
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README.md
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@@ -128,7 +128,6 @@ doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
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doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
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cache_position = torch.arange(0, len(doc_texts))
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doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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with torch.no_grad():
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
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@@ -175,7 +174,6 @@ doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
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doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
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cache_position = torch.arange(0, len(doc_texts))
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doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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with torch.no_grad():
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
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doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
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cache_position = torch.arange(0, len(doc_texts))
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doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
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with torch.no_grad():
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
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doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
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cache_position = torch.arange(0, len(doc_texts))
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doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
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with torch.no_grad():
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output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
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doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
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