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Update README.md

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@@ -1,6 +1,12 @@
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  ---
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  license: creativeml-openrail-m
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- ---
 
 
 
 
 
 
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  ---
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  <h1 align='center' style='font-size: 36px; font-weight: bold;'>Sparrow</h1>
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  <h3 align='center' style='font-size: 24px;'>Blazzing Fast Tiny Vision Language Model</h3>
@@ -36,12 +42,12 @@ model = AutoModelForCausalLM.from_pretrained(
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  torch_dtype=torch.float16,
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  device_map="auto",
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  trust_remote_code=True)
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- tokenizer = AutoTokenizer.from_pretrained("ManishThota/Sparrow", trust_remote_code=True)
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  #function to generate the answer
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  def predict(question, image_path):
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  #Set inputs
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- text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{question}? ASSISTANT:"
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  image = Image.open(image_path)
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  input_ids = tokenizer(text, return_tensors='pt').input_ids.to('cuda')
 
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  ---
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  license: creativeml-openrail-m
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+ datasets:
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+ - liuhaotian/LLaVA-Pretrain
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+ - liuhaotian/LLaVA-CC3M-Pretrain-595K
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+ language:
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+ - en
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+ metrics:
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+ - bleu
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  ---
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  <h1 align='center' style='font-size: 36px; font-weight: bold;'>Sparrow</h1>
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  <h3 align='center' style='font-size: 24px;'>Blazzing Fast Tiny Vision Language Model</h3>
 
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  torch_dtype=torch.float16,
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  device_map="auto",
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  trust_remote_code=True)
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+ tokenizer = AutoTokenizer.from_pretrained("ManishThota/SparrowVQE", trust_remote_code=True)
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  #function to generate the answer
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  def predict(question, image_path):
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  #Set inputs
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+ text = f"USER: <image>\n{question}? ASSISTANT:"
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  image = Image.open(image_path)
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  input_ids = tokenizer(text, return_tensors='pt').input_ids.to('cuda')