SwiftTransformer: A New LLM Transformer Design — Building Smarter, Faster, and More Efficient Transformers
Author: Songnian Qian
Goal: Redesign the architecture of large language models to be more efficient, more scalable.
Why This Work
Today’s language models grow mainly by scaling size — more parameters, deeper layers, wider embeddings. But that approach faces limits: higher cost, slower inference, and less flexibility.
My research explores a dynamic, hierarchical design for transformers that scales intelligently — through specialization, routing, and semantic feedback — rather than brute force.
This project is divided into eight parts, each solving a specific inefficiency in the current transformer design.
The Eight Parts of the New LLM Transformer Design
1. Next-N-Token Prediction with Semantic Coherence
Traditional models predict one token at a time. It is better if a learning system has a well-defined target. Next-token forces a single prediction when multiple are valid. My design predicts N tokens together (N ≈ 3–7) and evaluates them for semantic similarity, grammar, and logical flow. This improves contextual understanding and reduces over-penalization of valid paraphrases.
Concept validated and tested in inference, semantic loss implementation in progress.
2. Reduced Token Embedding Dimension for Attention-FFN Paths
Large embedding sizes drastically increase computation in all layers. Here, embeddings are divided into smaller, expert-routed paths, activated based on input sequence features. Routing uses multi-query gating (similar to multi-head attention) to pick the right subspace.
Without multi-path design, large embeddings are wasted computation.
3. Multi-Path Routing
GitHub: SwiftTransformer
Each token or sequence dynamically routes into the best-suited attention-FFN path — for example, code, math, creative writing, or dialogue.
A shared backbone captures general context, while each path specializes. Soft routing during training, hard routing at inference.
Working prototype implemented and benchmarked.
4. Adaptive Depth Selection
Not every query needs the full stack of layers. If coherence and confidence thresholds are met early, inference stops — saving computing while maintaining quality. This adaptive execution is inspired by ElasticBERT but enhanced with semantic-level stopping.
Planned integration with SpeedyGate routing.
5. Attention Type Pool Selection
Each transformer layer dynamically chooses the best attention mechanism.
Global reasoning, Sliding-window, Local dependencies, Long context and Balanced usage
Design complete; experiments scheduled for large-context datasets.
6. Mixture-of-Experts FFN — “SpeedyGate” (Breaking the O(E²) Bottleneck)
In GPT-style transformers, the MLP (FFN) layer is the largest and slowest block. SpeedyGate replaces it with a pool of lightweight experts (FiLM, ReGLU, percN) and a gating network that routes tokens:
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Soft routing → stable training
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Hard routing → fast inference
Results:
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3–4× faster inference -
Perplexity drop: 1844 → 34 -
Accuracy: 6% → 35%
Fully implemented and tested
7. Multiple Specialized LM Heads — “Fast-K” Sparse Inference
Fast-K replaces the single LM head with P parallel heads. During training, only the head that best predicts the gold token updates (token-aware sparse learning). During inference:
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A pilot head proposes the Top-K tokens.
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Other heads score only those K candidates.
This reduces output projection cost from O(P×V) → O(V + (P–1)×K).
Results:
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65% faster generation
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Perplexity: 25.66 → 16.11
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Better rare-token accuracy and contextual specialization
Architecture complete and validated. 
Next step: implement reduced vocabulary headers.
8. Evaluation Metrics — Semantic Perplexity Plus (sPPL⁺)
Traditional perplexity ignores meaning — it penalizes “vehicle” vs. “car.” sPPL⁺ introduces semantic, syntactic, and logical weighting.
It computes loss over overlapping N-token windows and adjusts probabilities by semantic similarity between predicted and reference embeddings.
Prototype implemented on small GPT-2 models; calibration for long-context tasks in progress. Will integrate with SpeedyGate + Fast-K systems for semantic-aware evaluation.
Summary of Results
Multi-Path Routing Done
SpeedyGate MoE FFN Done
Fast-K LM Heads Done
The Bigger Picture
This work follows one principle:
“Efficiency is not about doing less — it’s about doing the right work at the right time.”
While individual components may have been developed in other models, this design integrates all of them systematically as open source with comprehensive implementation details.
GitHub Repository
SwiftTransformer Includes:
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Multi-Path Attention-FFN Framework
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SpeedyGate Mixture-of-Experts FFN
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Fast-K Multi-Header LM Head
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Early implementation of sPPL⁺ Evaluation Metric
Closing Thoughts
The next generation of LLMs won’t just be bigger — they’ll be smarter in structure. By combining routing, sparse activation, and semantic feedback, we can build models that reason more like humans do: efficiently, contextually, and meaningfully.