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@@ -31,6 +31,9 @@ We evaluate GRM on the [reward model benchmark](https://huggingface.co/spaces/al
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  ## Usage
 
 
 
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  ```
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  import torch
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -56,7 +59,6 @@ with torch.no_grad():
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  reward = reward_tensor.cpu().detach().item()
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  ```
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- **Note: loading llama3 model into 8 bit could lead to performance degradation.**
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  ## Citation
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  If you find this model helpful for your research, please cite GRM
 
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  ## Usage
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+ **Note 1: Please download the `model.py` file from this repository to ensure the structure is loaded correctly and verify that the `v_head` is properly initialized.**
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+
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+ **Note 2: loading llama3 model into 8 bit could lead to performance degradation.**
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  ```
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  import torch
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
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  reward = reward_tensor.cpu().detach().item()
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  ```
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  ## Citation
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  If you find this model helpful for your research, please cite GRM