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Update README.md

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corrected Axolotl Version

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@@ -30,7 +30,6 @@ The 25% layer selection ensures minimal computational overhead for fine-tuning.
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  ## Training:
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  - Trained on **2x A40s (48GB VRAM each)** for over 1 hour using the **Axolotl**.
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- - Fine-tuning aimed to optimize the balance between model performance and resource efficiency, demonstrating how targeted spectrum fine-tuning can deliver substantial improvements without the need for full-scale model adjustments.
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  ### Training hyperparameters
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  ### Framework versions
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- - Axolotl 0.4.0
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  - Transformers 4.44.2
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  - Pytorch 2.4.0+cu121
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  - Datasets 2.20.0
 
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  ## Training:
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  - Trained on **2x A40s (48GB VRAM each)** for over 1 hour using the **Axolotl**.
 
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  ### Training hyperparameters
 
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  ### Framework versions
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+ - Axolotl 0.4.1
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  - Transformers 4.44.2
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  - Pytorch 2.4.0+cu121
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  - Datasets 2.20.0