🇹🇷 Laya-TR: Non-Autoregressive Decision & Reasoning Model for Turkish

Laya-TR is the first Turkish non-autoregressive decision and reasoning model, specifically engineered for ultra-low-latency decision making, candidate selection, and agentic routing.

While conventional generative Large Language Models (LLMs) generate tokens sequentially—taking hundreds to thousands of milliseconds to reach a decision—Laya-TR evaluates all candidate options and context simultaneously in a single parallel neural forward pass with sub-10ms latency (<10 ms).

With native Hugging Face AutoModel support, developers can deploy and run Laya-TR with standard transformers code without having to manage external architecture files or local repositories.

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⚡ Key Highlights

• Architecture: 22-layer mmBERT-base (ModernBERT backbone with GeGLU, Rotary Position Embeddings, and sliding-window attention) + 2-layer Decision Transformer Head + Shared Option Marker Scorer + Act/Escalate Head.
• Model Size: ~322 Million parameters (Compact, edge-ready, and exceptionally fast on a single GPU or CPU).
• Inference Latency: ~9.78 ms per question on a single GPU (95 – 162 decisions/second throughput).
• Training Efficiency: Trained in just 10.4 minutes (621 seconds) on a single NVIDIA GeForce RTX 4090 GPU.
• Seamless Hugging Face Integration: Fully compatible with AutoModel.from_pretrained("TurkishCodeMan/laya-tr", trust_remote_code=True).

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📊 Comprehensive Benchmark: MMLU-Pro TR

Laya-TR was evaluated on the complete test split of bezir/MMLU-pro-TR, representing the most demanding Turkish academic decision and multi-choice reasoning benchmark (11,842 Questions, 10 Choices A–J per question).

💡 Baseline Context: On a 10-choice multiple-choice test, the random guessing baseline is 10.00%.

Metric / Model	Base Laya (Zero-Shot)	Laya-TR (Fine-Tuned)	Net Gain / Relative Improvement
Total Test Questions	11,842	11,842	Full Test Split
Correct Answers	1,383 / 11,842	2,238 / 11,842	+855 More Correct Answers
Overall Accuracy	11.68%	18.90%	+7.22% Net (+61.82% Relative Jump) 🚀
Average Latency	5.54 ms	9.78 ms	Sub-10 Millisecond Decisions
Throughput	162.0 q/s	95.7 q/s	Real-Time Production Ready

📚 Category Breakdown Across All 14 Disciplines

Category	Total Questions	Base Laya (Zero-Shot)	Laya-TR (Fine-Tuned)	Relative Improvement
🧠 Psychology	780	11.28%	26.54%	+135.3% 🚀
🔬 Biology	714	13.31%	26.47%	+98.9% 🚀
🏛️ History	342	13.16%	24.56%	+86.6% 🚀
🩺 Health & Medicine	800	11.50%	24.00%	+108.7% 🚀
📈 Economics	830	14.58%	23.73%	+62.8% 🚀
🌐 Other	915	10.82%	22.51%	+108.0% 🚀
📜 Philosophy	479	12.11%	20.46%	+69.0%
💻 Computer Science	397	11.84%	20.15%	+70.2%
⚖️ Law	1086	11.42%	17.50%	+53.2%
💼 Business	774	12.02%	16.41%	+36.5%
🧪 Chemistry	1126	12.43%	14.56%	+17.1%
📐 Mathematics	1345	11.08%	14.05%	+26.8%
⚙️ Engineering	965	11.92%	13.99%	+17.4%
⚛️ Physics	1289	9.08%	13.96%	+53.7%

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🛠️ Training Strategy & Methodology

1. Curated Turkish Decision & Reasoning Corpus:
   • The model was fine-tuned on a curated, high-quality Turkish multi-domain decision dataset comprising 15,459 samples covering sciences, humanities, law, economics, and analytical reasoning.
2. Differential Learning Rates:
   • To safeguard the rich multilingual language representations of the mmBERT-base ModernBERT encoder, the backbone was fine-tuned with a conservative learning rate of $2 \times 10^{-5}$.
   • The Decision Transformer layers and the Option Marker Scorer head were trained with a 5x higher learning rate of $1 \times 10^{-4}$ to rapidly optimize candidate ranking and comparison.
3. Optimization & Stability:
   • AdamW optimizer with weight decay ($0.01$).
   • Cosine Annealing learning rate schedule preceded by linear warmup.
   • FP16 Automatic Mixed Precision (AMP) with gradient norm clipping ($1.0$).
4. Compute & Runtime:
   • Micro-batch size of 4 with 4 gradient accumulation steps (effective batch size of 16).
   • 3 epochs completed in 10.4 minutes (621.74 seconds) on a single consumer NVIDIA RTX 4090 GPU.

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🚀 Quickstart & Inference (Hugging Face AutoModel)

Install dependencies:

pip install torch transformers

Run inference in 3 lines of code:

from transformers import AutoModel, AutoTokenizer

# 1. Load model and tokenizer directly from Hugging Face Hub
model_id = "TurkishCodeMan/laya-tr"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id, trust_remote_code=True)

# 2. Define question and candidate options
question = "Türkiye Cumhuriyeti hangi yılda ilan edilmiştir?"
options = {
    "A": "1920",
    "B": "1923",
    "C": "1938",
    "D": "1919"
}

# 3. Predict in sub-10ms
result = model.decide(question=question, options=options, tokenizer=tokenizer)

print("Prediction :", result["prediction"])       # B
print("Option     :", result["selected_option"]) # B: 1923
print("Confidence :", f"{result['confidence']*100:.2f}%")
print("Latency    :", f"{result['latency_ms']:.2f} ms")
print("Full Probs :", result["probabilities"])

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🔄 Architectural Comparison

Dimension	Generative Autoregressive LLMs (7B - 70B)	Laya-TR (322M Decision Model)
Inference Paradigm	Sequential token-by-token generation	Single parallel neural forward pass
Latency per Decision	500 ms – 3,000 ms	~9.78 ms (<10 ms) ⚡
VRAM Consumption	16 GB – 80 GB	< 1.5 GB
Throughput	1 – 10 requests / sec	~100+ decisions / sec
Primary Use Cases	Text generation, creative writing, chat	Routing, classification, agent decisions, QA

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⚖️ License & Acknowledgments

• License: Apache 2.0
• Model Author: TurkishCodeMan
• Base Architecture: ConvAI Laya & ModernBERT
• Benchmark Reference: bezir/MMLU-pro-TR