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@@ -77,7 +77,8 @@ print(scores) # [0.00027803096387751553, 0.9948403768236574]
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  ```python
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  from FlagEmbedding import FlagLLMReranker
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- reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # Setting use_bf16 to True speeds up computation with a slight performance degradation
 
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  score = reranker.compute_score(['query', 'passage'])
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  print(score)
@@ -90,7 +91,8 @@ print(scores)
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  ```python
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  from FlagEmbedding import LayerWiseFlagLLMReranker
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- reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # Setting use_bf16 to True speeds up computation with a slight performance degradation
 
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  score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
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  print(score)
@@ -230,7 +232,7 @@ def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
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  return_tensors='pt',
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  )
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- tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
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  model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
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  model = model.to('cuda')
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  model.eval()
 
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  ```python
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  from FlagEmbedding import FlagLLMReranker
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+ reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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+ # reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
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  score = reranker.compute_score(['query', 'passage'])
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  print(score)
 
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  ```python
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  from FlagEmbedding import LayerWiseFlagLLMReranker
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+ reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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+ # reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
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  score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
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  print(score)
 
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  return_tensors='pt',
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  )
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+ tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)
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  model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
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  model = model.to('cuda')
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  model.eval()