Summary Efficient Fine-Tuning with Long Context Sizes arxiv.org
9,854 words - PDF document - View PDF document
One Line
LongLoRA effectively extends context sizes in large language models with shift short attention and revisits parameter-efficient fine-tuning, leading to improved results in topic retrieval tasks and reduced computational costs.
Slides
Slide Presentation (12 slides)
Key Points
- LongLoRA is an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs) with limited computation cost.
- LongLoRA introduces shift short attention (S2-Attn), which enables context extension and saves computation.
- LongLoRA achieves strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B.
- LongLoRA is compatible with most existing techniques, like FlashAttention-2, and can be applied using optimization and infrastructure techniques for LLMs.
- LongLoRA reduces computational costs significantly while achieving comparable performance to full fine-tuning.
Summaries
33 word summary
LongLoRA extends context sizes in large language models efficiently using shift short attention and revisits parameter-efficient fine-tuning. It achieves strong empirical results, outperforms other models in topic retrieval tasks, and reduces computational costs.
79 word summary
LongLoRA is an efficient method for extending context sizes in large language models (LLMs). It incorporates shift short attention (S2-Attn) and revisits parameter-efficient fine-tuning for context expansion. LongLoRA achieves strong empirical results and is compatible with existing techniques. It introduces a dataset called LongQA for supervised fine-tuning and demonstrates comparable performance to full fine-tuning while reducing computational costs. Additionally, it outperforms other LLMs in topic retrieval tasks and allows for fine-tuning on a single machine, significantly reducing computational costs.
128 word summary
LongLoRA is an efficient approach for extending the context sizes of pre-trained large language models (LLMs). It introduces shift short attention (S2-Attn) and revisits parameter-efficient fine-tuning for context expansion. LongLoRA achieves strong empirical results and is compatible with existing techniques. To make it practical, a dataset called LongQA is collected for supervised fine-tuning. LongLoRA demonstrates comparable performance to full fine-tuning while reducing computational costs by using S2-Attn during fine-tuning. It achieves good performance on evaluation datasets and outperforms other LLMs with long contexts in topic retrieval tasks. Efficiency is a key aspect of LongLoRA as it allows for fine-tuning on a single machine, reducing computational costs by a large margin. Ablation studies and experiments further demonstrate the effectiveness and efficiency of LongLoRA in extending context length for LLMs.
446 word summary
LongLoRA is an efficient fine-tuning approach for extending the context sizes of pre-trained large language models (LLMs). It addresses the computational cost of training LLMs with long context sizes by introducing shift short attention (S2-Attn) and revisiting the parameter-efficient fine-tuning regime for context expansion. LongLoRA achieves strong empirical results on various tasks and is compatible with existing techniques.
To make LongLoRA practical, a dataset called LongQA is collected for supervised fine-tuning. It contains more than 3k long context question-answer pairs. The code, models, dataset, and demo for LongLoRA are available on GitHub.
In experiments, LongLoRA demonstrates comparable performance to full fine-tuning while significantly reducing computational costs. It achieves this by using S2-Attn during fine-tuning, which retains the original attention architecture during inference. Existing optimization and infrastructure techniques for LLMs can also be applied to LongLoRA.
LongLoRA fine-tunes LLaMA2 models with extended context lengths and achieves good performance on various evaluation datasets. For example, on the Proof-pile test set, LongLoRA achieves lower perplexity as the evaluation context length increases. It also outperforms other open LLMs with long contexts in topic retrieval tasks.
Efficiency is a key aspect of LongLoRA. It allows for the fine-tuning of LLaMA2 models on a single 8x A100 machine, with the maximum context length reaching 100k for 7B models, 64k for 13B models, and 32k for 70B models. LongLoRA reduces the computational cost by a large margin compared to full fine-tuning.
The efficiency of LongLoRA is further demonstrated by its FLOPs profile. It reduces FLOPs, especially when the context length is large, by breaking down the model into different components.
The study presents LongLoRA, a method for efficiently extending the context length of large language models (LLMs). The authors propose a shift short attention mechanism that approximates the standard self-attention pattern during training. This approach reduces GPU memory cost and training time compared to full fine-tuning, while maintaining minimal accuracy compromise.
The effectiveness of LongLoRA is demonstrated through experiments on long-sequence language modeling and retrieval in long contexts. The models achieve better perplexity with longer context sizes and perform well on retrieval tasks.
Ablation studies analyze the efficiency profile of LongLoRA, showing that the proportion of attention sharply increases as the context length increases. However, the use of S2-Attn reduces the proportion of attention compared to full attention.
The relationship between perplexity and fine-tuning steps is explored in an ablation study, revealing that full fine-tuning has a faster convergence at the beginning, but the final gap is not large. Different attention patterns during fine-tuning are also investigated.
In conclusion, LongLoRA is an efficient method for extending the context length of LLMs. It achieves comparable performance to full fine-tuning while significantly reducing computational costs.
572 word summary
LongLoRA is an efficient fine-tuning approach for extending the context sizes of pre-trained large language models (LLMs). It addresses the computational cost of training LLMs with long context sizes by introducing shift short attention (S2-Attn) and revisiting the parameter-efficient fine-tuning regime for context expansion. LongLoRA achieves strong empirical results on various tasks and is compatible with existing techniques.
To make LongLoRA practical, a dataset called LongQA is collected for supervised fine-tuning. It contains more than 3k long context question-answer pairs. The code, models, dataset, and demo for LongLoRA are available on GitHub.
In experiments, LongLoRA demonstrates comparable performance to full fine-tuning while significantly reducing computational costs. It achieves this by using S2-Attn during fine-tuning, which retains the original attention architecture during inference. Existing optimization and infrastructure techniques for LLMs can also be applied to LongLoRA.
LongLoRA fine-tunes LLaMA2 models with extended context lengths and achieves good performance on various evaluation datasets. For example, on the Proof-pile test set, LongLoRA achieves lower perplexity as the evaluation context length increases. It also outperforms other open LLMs with long contexts in topic retrieval tasks.
Efficiency is a key aspect of LongLoRA. It allows for the fine-tuning of LLaMA2 models on a single 8x A100 machine, with the maximum context length reaching 100k for 7B models, 64k for 13B models, and 32k for 70B models. LongLoRA reduces the computational cost by a large margin compared to full fine-tuning.
The efficiency of LongLoRA is further demonstrated by its FLOPs profile. It reduces FLOPs, especially when the context length is large, by breaking down the model into different components.
In conclusion, LongLoRA is an efficient fine-tuning approach that extends the context sizes of pre-trained LLMs. It achieves comparable performance to full fine-tuning while significantly reducing computational costs. LongLoRA is compatible with existing techniques and is practical for various tasks.
The study presents LongLoRA, a method for efficiently extending the context length of large language models (LLMs). The authors propose a shift short attention mechanism that approximates the standard self-attention pattern during training. This approach reduces GPU memory cost and training time compared to full fine-tuning, while maintaining minimal accuracy compromise. The models trained with shift short attention retain the original attention architecture during inference, making them compatible with existing infrastructure and optimization techniques.
The effectiveness of LongLoRA is demonstrated through experiments on long-sequence language modeling and retrieval in long contexts. The models achieve better perplexity with longer context sizes. The models also perform well on retrieval tasks, achieving comparable performance to state-of-the-art models.
The study investigates the maximum context length that can be fine-tuned on a single 8x A100 machine. The LLaMA2 7B, 13B, and 70B models are extended to context lengths of 100k, 65536, and 32768, respectively. However, some perplexity degradation is observed on small context sizes for the extended models.
Ablation studies are conducted to analyze the efficiency profile of LongLoRA. The breakdown of FLOPs in different types of layers shows that the proportion of attention sharply increases as the context length increases. However, the use of S2-Attn reduces the proportion of attention compared to full attention.
The relationship between perplexity and fine-tuning steps is explored in an ablation study. The results show that full fine-tuning has a faster convergence than low-rank training at the beginning, but the final gap is not large. The study also investigates different attention patterns during fine-tuning.
In conclusion, LongLoRA is an efficient method for extending the context length of L
823 word summary
LongLoRA is an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs) with limited computation cost. Training LLMs with long context sizes is computationally expensive, but LongLoRA speeds up the context extension in two ways. First, it introduces shift short attention (S2-Attn), which enables context extension and saves computation. S2-Attn can be implemented with just two lines of code in training. Second, LongLoRA revisits the parameter-efficient fine-tuning regime for context expansion. It finds that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA achieves strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. It extends models' context while retaining their original architectures and is compatible with most existing techniques, like FlashAttention-2.
To make LongLoRA practical, a dataset called LongQA is collected for supervised fine-tuning. It contains more than 3k long context question-answer pairs. The code, models, dataset, and demo for LongLoRA are available on GitHub.
In experiments, LongLoRA demonstrates comparable performance to full fine-tuning while significantly reducing computational costs. It achieves this by using S2-Attn during fine-tuning, which retains the original attention architecture during inference. Existing optimization and infrastructure techniques for LLMs can also be applied to LongLoRA.
LongLoRA fine-tunes LLaMA2 models with extended context lengths and achieves good performance on various evaluation datasets. For example, on the Proof-pile test set, LongLoRA achieves lower perplexity as the evaluation context length increases. It also outperforms other open LLMs with long contexts in topic retrieval tasks.
Efficiency is a key aspect of LongLoRA. It allows for the fine-tuning of LLaMA2 models on a single 8x A100 machine, with the maximum context length reaching 100k for 7B models, 64k for 13B models, and 32k for 70B models. LongLoRA reduces the computational cost by a large margin compared to full fine-tuning.
The efficiency of LongLoRA is further demonstrated by its FLOPs profile. It reduces FLOPs, especially when the context length is large, by breaking down the model into different components.
In conclusion, LongLoRA is an efficient fine-tuning approach that extends the context sizes of pre-trained LLMs. It achieves comparable performance to full fine-tuning while significantly reducing computational costs. LongLoRA is compatible with existing techniques and is practical for various tasks.
The study presents LongLoRA, a method for efficiently extending the context length of large language models (LLMs). The authors propose a shift short attention mechanism that approximates the standard self-attention pattern during training. This approach reduces GPU memory cost and training time compared to full fine-tuning, while maintaining minimal accuracy compromise. The models trained with shift short attention retain the original attention architecture during inference, making them compatible with existing infrastructure and optimization techniques.
The effectiveness of LongLoRA is demonstrated through experiments on long-sequence language modeling and retrieval in long contexts. In long-sequence language modeling, the models achieve better perplexity with longer context sizes. For example, increasing the context window size from 8192 to 32768 improves the perplexity of the LLaMA2 7B model from 2.72 to 2.50. Similar improvements are observed for the LLaMA2 13B model. The models also perform well on retrieval tasks, achieving comparable performance to state-of-the-art models.
The study also investigates the maximum context length that can be fine-tuned on a single 8x A100 machine. The LLaMA2 7B, 13B, and 70B models are extended to context lengths of 100k, 65536, and 32768, respectively. The results demonstrate promising performance on these extremely large settings. However, some perplexity degradation is observed on small context sizes for the extended models, which is a known limitation of position interpolation.
Ablation studies are conducted to analyze the efficiency profile of LongLoRA. The breakdown of FLOPs in different types of layers shows that the proportion of attention sharply increases as the context length increases. However, the use of S2-Attn reduces the proportion of attention compared to full attention.
The relationship between perplexity and fine-tuning steps is explored in an ablation study. The results show that full fine-tuning has a faster convergence than low-rank training at the beginning, but the final gap is not large. The study also investigates different attention patterns during fine-tuning. It is found that shifting between layers is acceptable, while setting all attention heads as pattern 1 or pattern 2 does not work well. Dilated attention performs well in full fine-tuning but not in low-rank adaptation, while fine-tuning with stride sparse attention is harmful.
In conclusion, LongLoRA is an efficient method for extending the context length of LLMs. It reduces GPU memory cost and training time while maintaining minimal accuracy compromise. The proposed shift short attention mechanism approximates the standard self-attention pattern during training and retains the original attention architecture during inference. The method achieves better perplexity in long-sequence language modeling and performs well in retrieval tasks. Ablation studies provide insights into the efficiency profile of LongLoRA and the effects of different attention patterns. Further research is planned to investigate the compatibility of LongLoRA with other types of LLMs and position encodings.