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Update README.md

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  1. README.md +14 -4
README.md CHANGED
@@ -68,14 +68,18 @@ import torch
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  from llm2vec_wrapper import LLM2VecWrapper as LLM2Vec
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  # Load the model - latent attention weights are automatically loaded!
 
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  model = LLM2Vec.from_pretrained(
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  base_model_name_or_path='lukeingawesome/llm2vec4cxr',
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- pooling_mode="latent_attention", # This automatically loads the trained weights
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  max_length=512,
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  enable_bidirectional=True,
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  torch_dtype=torch.bfloat16,
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  use_safetensors=True,
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- )
 
 
 
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  # Simple text encoding
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  report = "There is a small increase in the left-sided effusion. There continues to be volume loss at both bases."
@@ -125,12 +129,18 @@ import torch
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  from llm2vec_wrapper import LLM2VecWrapper as LLM2Vec
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  # Load model
 
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  model = LLM2Vec.from_pretrained(
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- 'lukeingawesome/llm2vec4cxr',
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  pooling_mode="latent_attention",
 
 
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  torch_dtype=torch.bfloat16,
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  use_safetensors=True,
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- )
 
 
 
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  # Medical text analysis
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  instruction = 'Determine the change or the status of the pleural effusion.'
 
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  from llm2vec_wrapper import LLM2VecWrapper as LLM2Vec
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  # Load the model - latent attention weights are automatically loaded!
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+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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  model = LLM2Vec.from_pretrained(
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  base_model_name_or_path='lukeingawesome/llm2vec4cxr',
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+ pooling_mode="latent_attention",
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  max_length=512,
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  enable_bidirectional=True,
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  torch_dtype=torch.bfloat16,
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  use_safetensors=True,
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+ ).to(device).eval()
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+
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+ # Configure tokenizer
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+ model.tokenizer.padding_side = 'left'
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  # Simple text encoding
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  report = "There is a small increase in the left-sided effusion. There continues to be volume loss at both bases."
 
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  from llm2vec_wrapper import LLM2VecWrapper as LLM2Vec
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  # Load model
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+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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  model = LLM2Vec.from_pretrained(
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+ base_model_name_or_path='lukeingawesome/llm2vec4cxr',
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  pooling_mode="latent_attention",
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+ max_length=512,
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+ enable_bidirectional=True,
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  torch_dtype=torch.bfloat16,
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  use_safetensors=True,
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+ ).to(device).eval()
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+
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+ # Configure tokenizer
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+ model.tokenizer.padding_side = 'left'
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  # Medical text analysis
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  instruction = 'Determine the change or the status of the pleural effusion.'