Papers by Anique Tahir
JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning (2024.acl-demos)
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| Challenge: | Large Language Models (LLMs) face significant memory constraints when fine-tuning large prompt sequences. |
| Approach: | They propose a framework for PEFT-compatible fine-tuning of large language models, leveraging distributed training. |
| Outcome: | The proposed framework improves performance 12x compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU. |