Universal Model Adapter Fusion (UMAF): A Framework for Cross-Architecture Capability Transfer and Fusion
Abstract
The Universal Model Adapter Fusion (UMAF) framework enables the transfer and fusion of capabilities across language models of diverse architectures and scales. By leveraging a universal latent space for capability representation, a size interpolator for scaling, a fusion module for dynamic combination, and an adapter generator for parameter adjustments, UMAF produces lightweight, architecture-agnostic adapters. This paper provides a robust theoretical foundation, detailed methodological clarifications, and expanded experimental validation. UMAF demonstrates significant potential for creating bespoke, efficient AI models with enhanced interpretability and modularity.
Large language models (LLMs) have achieved remarkable success in specialized tasks, yet transferring and combining their capabilities across models with differing architectures or scales remains a significant challenge. Existing methods, such as Low-Rank Adaptation (LoRA) (Hu et al., 2021), are constrained to models with identical architectures, limiting their applicability in heterogeneous model ecosystems. The Universal Model Adapter Fusion (UMAF) framework addresses this gap by providing a systematic approach to extract, scale, fuse, and transfer capabilities across diverse models. This paper presents a refined and validated methodology, incorporating theoretical rigor, methodological clarity, and comprehensive experimental results.
We define a capability as a model’s measurable functional strength in a specific domain. Formally, a capability ( c ) is represented as a tuple ( (t, p) ), where:
- ( t ) denotes a task (e.g., reasoning, storytelling),
- ( p ) represents a performance metric (e.g., accuracy, BLEU score).
This definition ties capabilities to specific, measurable outcomes, distinguishing them from general model performance.
The universal latent space is a 512-dimensional embedding space designed to represent capabilities independently of model architecture. Its universality is supported by:
- Contrastive Learning: Representations are aligned based on functional similarity, following principles from (Bengio et al., 2013).
- Empirical Evidence: Experiments (Section 5.2) demonstrate that models with similar capabilities cluster together across architectures, validated using PCA and t-SNE visualizations.
Capabilities do not scale linearly with model size. Building on scaling laws (Kaplan et al., 2020), we model capability performance as ( p \propto N^\alpha ), where ( N ) is the parameter count and ( \alpha ) is a task-specific exponent. The Size Interpolator empirically learns this non-linear scaling relationship.
The Capability Extractor maps model activations into the universal latent space using a transformer encoder trained with contrastive learning and the InfoNCE loss: [ L = -\log \frac{\exp(\text{sim}(z_i, z_j)/\tau)}{\sum_{k=1}^N \exp(\text{sim}(z_i, z_k)/\tau)} ]
- Dataset Construction: Models (e.g., LLaMA, GPT-2) are labeled by their performance on benchmark tasks (e.g., MMLU, TriviaQA). "Similar capabilities" are defined as models with performance differences ( \leq 5% ) on a given task.
- Validation: Section 5.2 confirms the extractor’s ability to produce architecture-agnostic capability representations.
The Size Interpolator adjusts capability fingerprints to account for differences in model scale.
- Training Approach: Leverages naturally occurring model variants (e.g., 1B, 3B, 8B LLaMA models) to capture real-world scaling dynamics.
- Non-linear Scaling: A neural network learns task-specific ( \alpha ) values to model capability scaling accurately.
The Latent Fusion Module combines multiple capability fingerprints into a unified representation:
- Weighted Averaging: User-defined weights prioritize desired capabilities.
- Task-Specific Gating: A gating network dynamically adjusts weights based on task embeddings, enhancing flexibility.
The Adapter Generator transforms fused capability fingerprints into lightweight, LoRA-like adapters.
- Mechanism: A Reverse Translator MLP maps fingerprints to parameter adjustments, trained on pairs of fingerprints and fine-tuned adapters. Singular Value Decomposition (SVD) with rank ( r = 16 ) approximates parameter differences.
- Justification: Experimental results (Section 5.1) confirm effective capability transfer across architectures.
We evaluate UMAF across six distinct architectures:
- LLaMA,
- Qwen,
- GPT-2,
- T5,
- Mixture of Experts (MoE),
- Mamba.
Five capabilities are assessed:
- Reasoning (MMLU),
- Storytelling (StoryCloze),
- Factual knowledge (TriviaQA),
- Coding (HumanEval),
- Multilingual translation (WMT14).
UMAF is compared against:
- Direct LoRA transfer with rescaling,
- Fisher merging (Singh et al., 2020),
- Knowledge distillation (Hinton et al., 2015),
- Multi-task fine-tuning.
We perform ablation experiments to isolate component contributions:
- No Size Interpolator,
- Single-source fusion,
- Linear vs. non-linear interpolation,
- No Fusion Module.
For creative tasks (e.g., storytelling), three NLP graduate students rated outputs, achieving an inter-rater agreement of 0.75 (Cohen’s kappa). Results are statistically significant (p < 0.05).
- Training Time: 12 hours on 4 GPUs.
- Inference Overhead: +5% latency compared to baseline models.
UMAF outperforms baselines across tasks (error bars included):
- Reasoning (MMLU): 78% ± 2% (vs. 72% ± 3% Fisher merging),
- Storytelling (ROUGE-L): 0.58 ± 0.03 (vs. 0.45 ± 0.04 baseline),
- Coding (Pass@1): 42% ± 3% (vs. 35% ± 4%).
(Figure 1: Performance across tasks.)
PCA and t-SNE visualizations confirm that models cluster by capability rather than architecture (Figure 2).
Testing incompatible capability transfers (e.g., reasoning into a translation-only model) yields expected failures, delineating UMAF’s operational boundaries.
Fusing reasoning and storytelling capabilities maintains distinct performance: 76% MMLU and 0.56 ROUGE-L.
Transferred capabilities remain stable after 100 epochs of unrelated fine-tuning, indicating robustness.
Transfers from Transformer-based models to MoE architectures reduce efficiency by 15% ± 2%, highlighting a key limitation.
We derive capability archetypes by applying k-means clustering to latent fingerprints from over 50 models. Cluster centroids represent idealized capability profiles, offering a novel interpretive lens.
The Universal Model Adapter Fusion (UMAF) framework provides a robust and interpretable solution for transferring and fusing capabilities across heterogeneous language models. By addressing architectural and scaling challenges, UMAF enables the creation of efficient, modular AI systems. Future research will focus on improving scalability and mitigating cross-architectural inefficiencies.