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README.md
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@@ -27,7 +27,7 @@ LoRA Fine-Tuning: Low-Rank Adaptation (LoRA) enhances parameter efficiency, allo
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4-Bit Quantization: Memory usage is significantly reduced, making the model deployable on resource-constrained systems.
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The model is trained using the SFTTrainer library from the trl package, with parameters optimized for accuracy and resource efficiency.
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4-Bit Quantization: Memory usage is significantly reduced, making the model deployable on resource-constrained systems.
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Gradient Checkpointing: Further optimizations for handling long sequences and reducing GPU memory usage.
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The model is trained using the SFTTrainer library from the trl package, with parameters optimized for accuracy and resource efficiency.
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