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@@ -224,8 +224,8 @@ The model achieved strong performance on code review tasks, particularly excelli
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  The model demonstrates excellent capability in identifying and fixing common Python code issues, with particular strength in security vulnerability detection and code quality improvements.
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  ## Environmental Impact
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- Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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- - Hardware Type: NVIDIA A100 or equivalent
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  - Hours used: ~1.5 hours
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  - Training Approach: QLoRA for efficient fine-tuning
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@@ -237,7 +237,7 @@ Carbon emissions can be estimated using the Machine Learning Impact calculator p
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  ### Compute Infrastructure
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  **Hardware**
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- - Training performed on GPU cluster with NVIDIA A100/A6000 class hardware
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  **Software**
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  - Transformers, PEFT, TRL, BitsAndBytes
@@ -245,19 +245,19 @@ Carbon emissions can be estimated using the Machine Learning Impact calculator p
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  ## Citation
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  @misc{alen_philip_george_2025,
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- author = { Alen Philip George },
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- title = { Code_Review_Assistant_Model (Revision 233d438) },
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- year = 2025,
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- url = { https://huggingface.co/alenphilip/Code_Review_Assistant_Model },
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- doi = { 10.57967/hf/6836 },
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- publisher = { Hugging Face }
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  }
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  ## Model Card Authors
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  - Alen Philip George
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  ## Model Card Contact
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  Hugging Face: alenphilip
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- LinkedIn: linkedin.com/in/alen-philip-george-130226254
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  Email: alenphilipgeorge@gmail.com
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  The model demonstrates excellent capability in identifying and fixing common Python code issues, with particular strength in security vulnerability detection and code quality improvements.
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  ## Environmental Impact
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact/#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ - Hardware Type: NVIDIA H100 80GB VRAM
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  - Hours used: ~1.5 hours
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  - Training Approach: QLoRA for efficient fine-tuning
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  ### Compute Infrastructure
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  **Hardware**
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+ - Training performed on GPU cluster with NVIDIA H100 80GB VRAM
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  **Software**
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  - Transformers, PEFT, TRL, BitsAndBytes
 
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  ## Citation
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  @misc{alen_philip_george_2025,
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+ author = {Alen Philip George},
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+ title = {Code_Review_Assistant_Model (Revision 233d438)},
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+ year = 2025,
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+ url = {https://huggingface.co/alenphilip/Code_Review_Assistant_Model},
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+ doi = {10.57967/hf/6836},
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+ publisher = {Hugging Face}
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  }
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  ## Model Card Authors
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  - Alen Philip George
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  ## Model Card Contact
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  Hugging Face: alenphilip
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+ LinkedIn: [alenphilipgeorge](linkedin.com/in/alen-philip-george-130226254)
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  Email: alenphilipgeorge@gmail.com
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