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<?xml version="1.0" encoding="utf-8"?> |
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<!DOCTYPE body [ |
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<!ENTITY warning "Warning: Something bad happened... please refresh and try again."> |
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]> |
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<body> |
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<query rank="0"> |
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<title>System Message</title> |
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<text> |
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Now you are a researcher in the field of AI with innovative and pioneering abilities. |
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</text> |
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</query> |
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<query rank="1"> |
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<title>User Message</title> |
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<text> |
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# Task Description: |
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You are an AI researcher conducting studies in a specific domain. Someone has provided you with a methodology section, and your task is to transform it into another style. I will give you an example. The example begins with "# Example 1" and includes a Example Summarized Methods. Then, your task starts with "# Your Task", containing "Your Methodology Section". Your job is to transform Your Methodology Section into a Summarized Methods by referring to Example 1. Note that the ideas in Example 1 are unrelated to your idea, so the key focus should be on the style of Example Summarized Methods. You should directly start with your response and do not start with a section title like "## Your Summarized Methods". |
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# Example 1 |
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## Example Summarized Methods |
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Building on the concept of Parameter-Efficient Fine-Tuning (PEFT), which aims to adapt Large Language Models (LLMs) to specific tasks without the full computational cost of training all parameters, the following algorithm integrates a memory-efficient strategy enhanced through quantization and an auxiliary side network. This allows for efficient fine-tuning and inference on large models with reduced memory requirements and computational demands. |
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1. **Quantization of LLM to 4-bit Precision:** |
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1. Utilize 4-bit quantization to reduce the memory footprint of the LLM's weights. Each floating-point parameter in the LLM is converted to a 4-bit representation for efficient memory utilization. |
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2. Begin with the conversion of 16-bit parameters to 4-bit using the relation: |
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\[ |
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X_{{4bit}} = \text{{round}}\left(\frac{{M_{{4bit}}}}{{\text{{Absmax}}(X_{{16bit}})}} \cdot X_{{16bit}}\right) |
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\] |
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where \(M_{{4bit}}\) is the maximum value representable in 4 bits, ensuring quantization minimizes precision loss by managing outliers through block-wise separate quantization. |
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2. **Side Network for Memory-Efficient Tuning:** |
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1. Implement a side network \(g\) with dimensions reduced by a factor \(r\) relative to the original model \(f\). The side network processes information more economically, storing less data and reducing computational load during training. |
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2. Define the hidden state transformation at the \(i\)-th layer as: |
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\[ |
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h_{{gi}}^{{16bit}} = (1 - \beta_i) * \text{{downsample}}_i(h_{{fi}}^{{16bit}}) + \beta_i * h_{{gi-1}}^{{16bit}} |
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\] |
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where \(\beta_i\) is a learnable gating parameter and \(\text{{downsample}}_i\) reduces dimensionality. |
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3. **Low-Memory Gradient Calculation:** |
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1. Perform backpropagation limited to the side network \(g\), excluding the calculation of gradients for the quantized weights in \)f\), leveraging the pre-trained knowledge while focusing computational resources on the task-specific adaptation. |
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2. The gradient computation is detached from the main LLM, avoiding the costly backpropagation through large transformer layers and focusing updates through efficient gradient paths within \(g\). |
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4. **Combining Outputs for Inference:** |
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1. At inference, blend the outputs from the LLM and side network as a weighted sum: |
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\[ |
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h_N^{{16bit}} = \alpha h_{{fN}}^{{16bit}} + (1-\alpha) h_{{gN}}^{{16bit}} |
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\] |
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where \(\alpha\) is a learnable parameter initialized to prioritize the pre-trained model influence gradually allowing task customization through tuning. |
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5. **Optimized Training Procedure:** |
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1. Integrate efficient downsampling techniques, such as LoRA and Adapter models, reducing parameter size significantly without losing efficacy. |
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2. Maintain a 16-bit floating-point data type for computations in forward and backward passes to balance precision and performance, ensuring that the quantized network remains robust and generalizable. |
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By synthesizing quantization with an auxiliary network, the algorithm achieves a robust parameter-efficient fine-tuning technique, significantly reducing memory overhead, improving inference speed, and maintaining high performance despite the minimal parameters being updated. This approach effectively supports large-scale models, facilitating application in environments with constrained computational resources. |
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# Your Task |
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## Your Methodology Section |
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{methodology} |
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## Your Summarized Methods |
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</text> |
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</query> |
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</body> |
