Files changed (2) hide show
  1. README.md +9 -5
  2. config.json +1 -1
README.md CHANGED
@@ -27,7 +27,7 @@ Resources and Technical Documentation:
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  ## How to Get Started with the Model
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- Use the code below to get started with the model.
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  ```python
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  import requests
@@ -35,8 +35,10 @@ import requests
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  from PIL import Image
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  from transformers import AutoProcessor, AutoModelForCausalLM
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- model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  prompt = "<OD>"
@@ -44,7 +46,7 @@ prompt = "<OD>"
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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  image = Image.open(requests.get(url, stream=True).raw)
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- inputs = processor(text=prompt, images=image, return_tensors="pt")
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  generated_ids = model.generate(
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  input_ids=inputs["input_ids"],
@@ -77,8 +79,10 @@ import requests
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  from PIL import Image
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  from transformers import AutoProcessor, AutoModelForCausalLM
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- model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
@@ -89,7 +93,7 @@ def run_example(task_prompt, text_input=None):
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  prompt = task_prompt
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  else:
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  prompt = task_prompt + text_input
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- inputs = processor(text=prompt, images=image, return_tensors="pt")
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  generated_ids = model.generate(
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  input_ids=inputs["input_ids"],
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  pixel_values=inputs["pixel_values"],
 
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  ## How to Get Started with the Model
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+ Use the code below to get started with the model. All models are trained with float16.
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  ```python
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  import requests
 
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  from PIL import Image
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  from transformers import AutoProcessor, AutoModelForCausalLM
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+ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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+ torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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+ model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
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  processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  prompt = "<OD>"
 
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
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  image = Image.open(requests.get(url, stream=True).raw)
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+ inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
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  generated_ids = model.generate(
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  input_ids=inputs["input_ids"],
 
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  from PIL import Image
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  from transformers import AutoProcessor, AutoModelForCausalLM
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+ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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+ torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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+ model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
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  processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
 
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  prompt = task_prompt
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  else:
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  prompt = task_prompt + text_input
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+ inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
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  generated_ids = model.generate(
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  input_ids=inputs["input_ids"],
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  pixel_values=inputs["pixel_values"],
config.json CHANGED
@@ -79,7 +79,7 @@
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  "image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"]
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  },
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  "vocab_size": 51289,
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- "torch_dtype": "float32",
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  "transformers_version": "4.41.0.dev0",
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  "is_encoder_decoder": true
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  }
 
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  "image_feature_source": ["spatial_avg_pool", "temporal_avg_pool"]
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  },
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  "vocab_size": 51289,
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+ "torch_dtype": "float16",
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  "transformers_version": "4.41.0.dev0",
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  "is_encoder_decoder": true
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  }