LLM Optimization & Fine-tuning
FLORA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
This paper introduces FLORA, a family of fused forward-backward adapters (FFBA) for parameter-efficient fine-tuning of LLMs on downstream tasks. By fusing forward and backward adapters into existing projection layers, FLORA significantly reduces inference-time latencies while maintaining or improving accuracy compared to conventional LoRA.
Executive Impact: Speed & Efficiency Redefined
Our analysis reveals how FLORA radically reduces inference-time latency while maintaining or improving accuracy across critical enterprise tasks.
0%
TPOT Latency Reduction
0%
LoRA Overhead Reduction
Deep Analysis & Enterprise Applications
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Introduction to PEFT
Large language models (LLMs) have revolutionized the world of artificial intelligence with downstream applications in almost all possible domains that humans can imagine (OpenAI and et.al., 2023). With an ever-growing demand to accommodate more domains and newer tasks, and with prohibitively high retraining or full fine-tuning (FFT) costs, parameter-efficient fine-tuning (PEFT) (Mangrulkar et al., 2022) of LLMs using adapters has become the most commonly used approach to add more capabilities to existing LLMs (Houlsby et al., 2019a). Also, the performance of LLMs fine-tuned on individual tasks with separate adapters is often better than that of a generalized LLM that can handle multiple tasks.
LoRA & Adapters
LLM adapters can be broadly classified into prompt or prefix fine-tuning, serial adapters, and parallel adapters (Hu et al., 2023). Among the different types of adapters proposed in the literature, low-rank adapters (LoRA) and their variants are widely used to fine-tune LLMs (Hu et al., 2022; Li and Liang, 2021a; Hu et al., 2023). Serial adapters have been one of the earliest adapters used in the literature of neural networks in several domains, including natural language processing (Rebuffi et al., 2017; Houlsby et al., 2019a). However, one main disadvantage of the serial adapters is the sequential nature of these adapter computations, which cannot be easily parallelized along with the base model computations, leading to significant latency overheads as compared to using only the base models. Low-rank adapters (LoRA), which have become more popular in recent times, are a type of parallel adapters that are attached in parallel to any linear projection rather than at block or higher levels. The popularity of LoRA and it's variants stem from their simplicity and ability to merge them back into the base model easily. Prompt or prefix fine-tuning is an alternative way to adapt LLMs to new tasks with minimal compute overhead, but are generally seen to underperform serial or parallel adapters (Hu et al., 2023).
Fused Adapters (FLORA)
Conventional LoRA uses low-rank approximation (LRA) in order to process information efficiently in a typically large hidden input dimension. For instance, the output of a linear projection layer with weights W∈ Rdoxdi and LoRA adapters A∈Rrxdi, B∈Rdo×r, for an input X ∈ Rdi×L is given by Z = WX + BAX (1) where di and do are the input and output dimensions, L is the input sequence length, and r(
21-48%
TPOT Latency Reduction (1B & 3B models)
Enterprise Process Flow
LoRA: Separate Adapter Computation
→
Partially-fused LoRA: WX, AX Fused
→
Fused Forward Adapter (FFA): B Removed, A Expanded
→
Fused Forward-Backward Adapter (FFBA/FLORA): B Fused, Parallel Input/Output
| Feature | LoRA | FLORA |
|---|---|---|
| Inference Latency (TPOT) | Higher (21-30% / 31-48% overhead) | Reduced (7-8% / 7-11% reduction) |
| Commonsense Reasoning | Good | Comparable or marginally better |
| Arithmetic Reasoning | Good | Similar or better |
| Summary/Dialogue Tasks | Good | Significantly better |
| Parameter Efficiency | High | High |
Why FLORA Matters for Enterprise LLMs
In enterprise AI, LLM deployment often faces critical constraints related to inference speed and operational cost. FLORA directly addresses these challenges by reducing inference-time latencies significantly compared to traditional LoRA. This means faster responses for customer-facing applications and lower GPU costs for high-throughput tasks.
Furthermore, FLORA's performance gains, especially in summary and dialogue generation, make it highly suitable for enterprise applications such as automated customer support, document summarization, and interactive AI assistants, where both accuracy and speed are paramount. Its ability to fuse adapter computations into existing model layers allows for seamless integration and deployment without sacrificing performance or increasing complexity.
Advanced ROI Calculator
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Your Enterprise AI Implementation Roadmap
A phased approach ensures seamless integration and maximum impact with FLORA. Each step is designed for clarity and efficiency.
Phase 1: Initial Assessment & Setup
Evaluate existing LLM infrastructure, identify key use cases for PEFT, and set up the FLORA environment.
Phase 2: Adapter Fine-tuning & Optimization
Fine-tune FLORA adapters on specific enterprise datasets, optimizing for performance and latency reduction.
Phase 3: Integration & Deployment
Integrate FLORA-tuned LLMs into production systems, focusing on latency-critical applications like chatbots and real-time analytics.
Phase 4: Monitoring & Iteration
Continuously monitor performance, gather feedback, and iterate on adapter designs for ongoing improvements and new tasks.
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