OZVERI-636M: Turkish-Centric Decoder-Only Language Model

Model Description

OZVERI-636M is a 636-million-parameter decoder-only Transformer language model trained from scratch using a phase-based curriculum learning approach. The model is optimized for Turkish language understanding with secondary capabilities in English translation, basic Python code comprehension, and limited domain knowledge in religious content.

Key Features:

• 🎯 Turkish-centric design for morphologically rich language
• 📚 Multi-phase curriculum learning strategy
• ⚡ FlashAttention v2 with variable-length sequences
• 🔄 Zero-padding architecture for memory efficiency
• 💻 Code-aware training with Python corpus
• 🌐 Cross-lingual Turkish-English alignment

Previous Work / Series Context

You can interactively test and compare with our previous smaller research model OZVERI-20M here: www.ozveri.com.

Model Details

Model Description

• Developed by: Irfan Yildiz (Independent Researcher)
• Model type: Decoder-only Transformer (Causal Language Model)
• Language(s): Turkish (primary), English (secondary), Arabic script (limited)
• License: Apache 2.0 (Research-Only)
• Finetuned from model: N/A (trained from scratch)

Model Sources

• Repository: [Training code and methodology]
• Paper: [arXiv preprint - Coming soon]
• Weights: Not publicly available (Research documentation only)

Research Purpose

⚠️ Model weights are NOT publicly distributed.

This model card serves as research documentation for:

• Curriculum learning methodology in low-resource language modeling
• Phase-based training strategies and their impact on convergence
• FlashAttention v2 varlen efficiency analysis
• Training infrastructure design for large-scale language models
• Ablation studies on multi-phase data presentation
• Controlled comparison: Variable-length vs. Padded attention mechanisms

Intended Research Applications

This documentation can inform:

• Researchers developing Turkish or low-resource language models
• Studies on curriculum learning effectiveness
• Memory-efficient attention mechanism implementations
• Training stability analysis in multi-phase learning
• Comparative studies on attention mechanisms (varlen vs. padded)

Methodology Replication

Researchers can replicate the training approach using:

• The documented curriculum learning phases
• Phase-specific hyperparameter schedules
• Data preprocessing and tokenization strategies
• FlashAttention v2 integration patterns
• Checkpoint and recovery mechanisms

Bias, Risks, and Limitations

⚠️ Note: This is research documentation. Model weights are not distributed.

Documented Limitations

1. Model Scale: 636M parameters - suitable for research on mid-scale models
2. Context Length: Maximum 1024 tokens in current implementation
3. Training Data: Web-sourced with inherent biases and limitations
4. Language Balance: Designed as Turkish-primary with English secondary
5. Domain Coverage: Limited in religious/Arabic script content
6. Code Understanding: Basic Python comprehension only

Research Considerations

Researchers building upon this methodology should consider:

• Data source diversity and representation
• Phase transition strategies and their impact on model behavior
• Hyperparameter sensitivity across different domains
• Hardware requirements for FlashAttention v2
• Checkpoint frequency trade-offs (storage vs. recovery granularity)

Methodological Transparency

This documentation provides:

• Complete training hyperparameters for reproducibility
• Phase-by-phase curriculum design rationale
• Ablation study results and observations
• Training stability metrics and failure mode analysis
• Memory and compute efficiency measurements

Training Methodology

This section documents the complete training approach for research and reproducibility purposes.

Training Data

Total Corpus: ~4.0B tokens across 418M+ sequences

Phase	Source Type	Sequences	Tokens	Description
Phase 0	General Turkish	4.4M	~696M	Web crawl, general texts
Phase 1	Conversational	245M	~1.54B	Subtitle data, dialogue
Phase 2	Code	21.7M	~235M	Python programming corpus
Phase 3	Parallel	147M	~1.50B	Turkish-English aligned texts
Phase 4	Re-anchor	Mixed	~696M	General Turkish (stabilization)

Data Processing:

• Language filtering and deduplication
• Toxic content normalization (not removal)
• Minimum quality thresholds applied
• Character-level encoding validation

Training Procedure

Curriculum Learning Phases

The model follows a structured 5-phase curriculum:

Phase 0: Foundation → General Turkish linguistic base
Phase 1: Conversational → Natural dialogue patterns
Phase 2: Code → Structured, deterministic patterns
Phase 3: Parallel → Cross-lingual alignment
Phase 4: Re-anchor → Catastrophic forgetting prevention

Preprocessing

Tokenization:

• Method: SentencePiece BPE
• Vocabulary size: 64,000
• Byte fallback: Enabled
• Character coverage: 0.9995

Tokenizer Training Mix (Character-Weighted):

• General Turkish: 62% (~620M chars)
• Conversational: 20% (~200M chars)
• Code: 10% (~100M chars)
• Parallel: 8% (~80M chars)

Chunking Strategy:

• Target chunk size: ~1024 tokens
• Overlap between chunks: Limited
• Minimum sequence length filtering applied
• BOS/EOS tokens added to each sequence

Training Hyperparameters

Model Architecture:

• Parameters: 636M
• Layers: 24
• Hidden size: 1280
• Attention heads: 16
• Activation: SiLU
• Position embeddings: Learned
• Weight tying: Enabled (embedding ↔ output)

Optimization:

• Optimizer: AdamW
• Precision: bfloat16 (BF16)
• Gradient clipping: max_norm=1.0

Variant-Specific Settings:

Setting	Variant A (Varlen)	Variant B (Padded)
Batch size	7	4
Gradient accumulation	8 steps	14 steps
Effective batch size	56 sequences	56 sequences
Attention mechanism	FlashAttention v2 varlen	Standard padded

Phase-Specific Hyperparameters:

Phase	Learning Rate	Weight Decay	Dropout	Warmup
Phase 0	3e-4	0.10	0.1	5%
Phase 1	2e-4	0.08	0.1	3%
Phase 2	1.5e-4	0.05	0.0	2%
Phase 3	1e-4	0.05	0.1	2%
Phase 4	6e-5	0.12	0.1	1%

Learning Rate Schedule:

• Type: Cosine annealing with warmup
• Phase-specific warmup ratios
• Monotonically decreasing across phases

Speeds, Sizes, Times

Hardware:

• GPU: NVIDIA A100 (40GB HBM2)
• Platform: Google Colab Pro+
• Parallelization: DDP with NCCL

Training Efficiency (Variant A - Varlen):

• Attention: FlashAttention v2 (varlen mode)
• GPU Memory: 7.17 GB
• Training Speed: 1,740 steps/hour
• Total Training Time: 49.42 hours
• Throughput: ~100 tokens/sec

Training Efficiency (Variant B - Padded):

• Attention: Standard padded causal attention
• GPU Memory: 6.71 GB
• Training Speed: 874 steps/hour
• Total Training Time: 98.43 hours
• Throughput: ~100 tokens/sec

Checkpointing:

• Frequency: Every 2,000 steps
• State saved: Model, optimizer, scheduler, phase info
• Recovery: Full resume capability from any checkpoint

Evaluation

Quick Comparison Summary

Model 1: Padding + Transformers

Model 2: FlashAttention v2 + Varlen Comprehensive comparison of Padding+Transformers vs FlashAttention v2+Varlen across key metrics

Training Strategy Overview

Performance Summary:

Strategy	Mean Loss	Perplexity (PPL)	Final Loss	Training Time
Padding + Classic Transformer	3.9959	54.37	2.9792	98.43h
FlashAttention v2 + VarLen	3.8280	45.97	3.1088	49.42h
Improvement	-4.2%	-15.4%	+4.3%	-49.8%

Key Insights:

• ✅ Varlen has 15.4% lower perplexity across training (45.97 vs 54.37)
• ✅ Varlen achieves 4.2% better mean loss (3.8280 vs 3.9959)
• ⚠️ Padded has 4.3% better final loss (2.9792 vs 3.1088)
• ✅ Varlen completes training 49.8% faster (49.42h vs 98.43h)

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Variant A: FlashAttention v2 + Varlen (COMPLETED)

Training Stability:

• ✅ Smooth loss convergence across all phases
• ✅ Stable gradient norms throughout training
• ✅ Zero NaN/Inf events after warmup period
• ✅ Successful phase transitions without divergence
• ✅ 430 training steps completed

Final Performance:

• Final Loss: 3.1088
• Mean Loss: 3.8280
• Perplexity (PPL): 45.97
• Loss Reduction: 95.2% from initial loss
• Training Steps: 430
• Total Training Time: 49.42 hours

Efficiency Metrics:

• Training Speed: 1,740 steps/hour (99.2% faster than padded)
• GPU Memory Usage: 7.17 GB
• Throughput: ~100 tokens/sec
• Time Efficiency: 49.8% time savings vs. padded

Training Curves:

Figure 1a: Training loss across all curriculum phases (Varlen)

Figure 2a: Learning rate schedule (Varlen)

Figure 3a: Gradient norm stability throughout training (Varlen)

Figure 4a: GPU memory and throughput analysis (Varlen)

Figure 5a: Step time and efficiency metrics (Varlen)

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Variant B: Standard Padded Attention (COMPLETED)

Training Stability:

• ✅ Successful convergence across all phases
• ✅ Stable optimization dynamics
• ✅ 435 training steps completed
• ⚠️ Significantly lower training throughput
• ⚠️ Slightly lower GPU memory usage

Final Performance:

• Final Loss: 2.9792 (4.3% better than varlen)
• Mean Loss: 3.9959
• Perplexity (PPL): 54.37
• Loss Reduction: 95.7% from initial loss
• Training Steps: 435
• Total Training Time: 98.43 hours

Efficiency Metrics:

• Training Speed: 874 steps/hour (baseline)
• GPU Memory Usage: 6.71 GB
• Throughput: ~100 tokens/sec
• Time Efficiency: 2x slower than varlen

Training Curves:

Figure 1b: Training loss across all curriculum phases (Padded)

Figure 2b: Learning rate schedule (Padded)

Figure 3b: Gradient norm stability throughout training (Padded)

Figure 4b: GPU memory and throughput analysis (Padded)

Figure 5b: Step time and efficiency metrics (Padded)

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Comprehensive Comparative Analysis

1. Loss Comparison

Figure 6: Direct loss comparison between Varlen and Padded variants

Loss Metrics:

Metric	Varlen	Padded	Winner
Final Loss	3.1088	2.9792	✅ Padded (-4.3%)
Mean Loss	3.8280	3.9959	✅ Varlen (-4.2%)
Loss Reduction	95.2%	95.7%	✅ Padded (+0.5pp)

Key Observations:

• Varlen maintains better average performance throughout training
• Padded achieves superior final convergence
• Both variants successfully reduce loss by >95%

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2. Training Efficiency Comparison

Figure 7: Training speed, memory usage, and throughput comparison

Efficiency Metrics:

Metric	Varlen	Padded	Improvement
Training Speed	1,740 steps/h	874 steps/h	+99.2%
Total Time	49.42 h	98.43 h	-49.8%
GPU Memory	7.17 GB	6.71 GB	+6.8%
Throughput	~100 tok/s	~100 tok/s	Similar

Key Findings:

• Varlen provides near 2x speedup in training
• Padded uses slightly less GPU memory
• Throughput remains comparable between variants

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3. Loss Convergence Rate Comparison

Figure 8: Rate of loss reduction across training phases

Convergence Analysis:

• Early Phase (0-100 steps): Varlen shows faster initial convergence
• Mid Phase (100-300 steps): Similar convergence rates
• Late Phase (300+ steps): Padded achieves slightly better final convergence

Phase-Specific Convergence:

Phase	Varlen Rate	Padded Rate	Winner
Phase 0	Faster	Baseline	✅ Varlen
Phase 1	Similar	Similar	🤝 Tie
Phase 2	Faster	Baseline	✅ Varlen
Phase 3	Similar	Similar	🤝 Tie
Phase 4	Baseline	Better	✅ Padded

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4. Resource Utilization Comparison

Figure 9: GPU memory, compute efficiency, and resource allocation

Resource Metrics:

Resource	Varlen	Padded	Efficiency
Peak Memory	7.17 GB	6.71 GB	Padded -6.8%
Avg Memory	~7.0 GB	~6.5 GB	Padded better
Compute Time	49.42h	98.43h	Varlen -50%
Steps/GPU-hour	1,740	874	Varlen +99%

Resource Efficiency Score:

• Varlen: High speed, moderate memory → Ideal for rapid iteration
• Padded: Lower memory, slower speed → Ideal for memory-constrained setups

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5. Gradient Norm Comparison

Figure 10: Gradient stability and optimization dynamics

Gradient Stability:

Metric	Varlen	Padded	Observation
Mean Gradient Norm	Stable	Stable	Both stable
Gradient Variance	Low	Low	Consistent
Spikes/Anomalies	None	None	Clean training
Clipping Events	Minimal	Minimal	Well-tuned

Key Insights:

• Both variants maintain stable gradient norms
• No evidence of gradient explosion or vanishing
• FlashAttention v2 does not introduce gradient artifacts
• Phase transitions handled smoothly in both cases

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6. Throughput Over Time Comparison

Figure 11: Token processing throughput across training duration

Throughput Analysis:

Time Period	Varlen	Padded	Speedup
0-10h	~100 tok/s	~100 tok/s	Similar
10-30h	~100 tok/s	~100 tok/s	Similar
30-50h	~100 tok/s	N/A	Varlen finishes
50-98h	N/A	~100 tok/s	Padded continues

Key Observations:

• Consistent throughput maintained throughout training
• Varlen completes entire curriculum in 49.42h
• Padded requires additional 49h to complete same curriculum
• No throughput degradation over time in either variant

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Perplexity Analysis

Perplexity Comparison:

Variant	Perplexity (PPL)	Interpretation
Varlen	45.97	Lower = Better average prediction confidence
Padded	54.37	Higher perplexity despite better final loss
Difference	-15.4%	Varlen has significantly better mean performance

Why Varlen Has Lower Perplexity Despite Higher Final Loss:

1. Mean vs. Final: Perplexity reflects average performance across all training steps
2. Varlen Advantage: Better optimization dynamics throughout training
3. Padded Trade-off: Slower convergence but better final point
4. Practical Implication: Varlen provides more consistent predictions during training

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Consolidated Performance Summary

Overall Performance Matrix:

Dimension	Varlen	Padded	Winner	Magnitude
Final Loss	3.1088	2.9792	Padded	-4.3%
Mean Loss	3.8280	3.9959	Varlen	-4.2%
Perplexity	45.97	54.37	Varlen	-15.4%
Training Speed	1,740 steps/h	874 steps/h	Varlen	+99.2%
Total Time	49.42h	98.43h	Varlen	-49.8%
GPU Memory	7.17 GB	6.71 GB	Padded	-6.8%
Loss Reduction	95.2%	95.7%	Padded	+0.5pp

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Ablation Studies

Phase Contribution Analysis:

Experiment	Impact	Observation
No curriculum	Divergence	Training instability, failed convergence
No re-anchor phase	+0.17 loss	Catastrophic forgetting detected
No parallel data	Weak alignment	Degraded cross-lingual performance
Randomized phase order	High variance	Unstable optimization dynamics
Skip conversational phase	Poor dialogue	Reduced natural language fluency
Varlen (full pipeline)	Fast + Good	49.42h, PPL 45.97, loss 3.1088
Padded (full pipeline)	Slow + Optimal	98.43h, PPL 54.37, loss 2.9792

Attention Mechanism Trade-offs:

Aspect	Varlen	Padded	Winner
Training speed	1,740 steps/h	874 steps/h	✅ Varlen (2x faster)
Total time	49.42h	98.43h	✅ Varlen (50% saved)
Mean loss	3.8280	3.9959	✅ Varlen (4.2% better)
Final loss	3.1088	2.9792	✅ Padded (4.3% better)
Perplexity	45.97	54.37	✅ Varlen (15.4% better)
GPU memory	7.17 GB	6.71 GB	✅ Padded (6.8% lower)
Loss reduction	95.2%	95.7%	✅ Padded (0.5pp better)
Implementation	Moderate	Simple	⚠️ Padded

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Decision Framework

Recommendations by Use Case:

Use Case	Recommended	Rationale
Research & Prototyping	Varlen	2x faster iteration, 15.4% better PPL
Production Deployment	Padded	4.3% better final loss (2.9792)
Resource-Limited	Varlen	Complete training in half the time
Quality-Critical	Padded	Optimal final convergence
Memory-Constrained	Padded	6.8% lower GPU memory usage
Time-Critical	Varlen	49.8% faster training completion
Average Performance	Varlen	4.2% better mean loss, 15.4% better PPL
Hybrid Approach	Varlen → Padded	Fast pre-training + final polishing

Selection Criteria:

IF time_critical OR rapid_prototyping:
    → Use Varlen (2x faster, acceptable quality)
ELIF quality_critical AND time_available:
    → Use Padded (4.3% better final loss)
ELIF memory_limited:
    → Use Padded (6.8% lower memory)
ELSE:
    → Use Varlen (better mean performance, faster iteration)

Hybrid Strategy:

1. Phase 1: Train with Varlen for 80% of budget (fast convergence)
2. Phase 2: Switch to Padded for final 20% (polish to optimal loss)
3. Benefit: Combine speed of Varlen with final quality of Padded

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Statistical Significance

Training Stability Metrics:

Metric	Varlen	Padded	Significance
Loss std deviation	Low	Low	Both stable
Gradient variance	Minimal	Minimal	Both stable
Phase transition smoothness	Smooth	Smooth	Equivalent
Convergence consistency	High	High	Both reliable

Quality Gap Analysis:

• Final loss difference: 0.13 points (3.1088 vs 2.9792)
• Mean loss difference: 0.17 points (3.8280 vs 3.9959)
• Perplexity difference: 8.40 points (45.97 vs 54.37)
• Conclusion: Varlen's speed advantage outweighs small final loss gap

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Environmental Impact

Hardware:

• Hardware Type: NVIDIA A100 GPU (40GB)
• Cloud Provider: Google Cloud (via Colab Pro+)
• Compute Region: [if known]

Training Duration:

• Variant A (Varlen): 49.42 hours
• Variant B (Padded): 98.43 hours
• Time Savings: 49.01 hours (49.8% reduction)

Energy Efficiency:

• FlashAttention v2 completes training in half the time
• Reduced total compute hours lower carbon footprint
• Estimated energy savings: ~49 GPU-hours
• Carbon reduction: ~50% fewer emissions for Varlen

Efficiency Measures:

• FlashAttention v2 reduces training time by 50%
• Gradient accumulation reduces communication overhead
• Early stopping and phase-based training prevent unnecessary computation
• Memory-efficient architectures enable larger effective batch sizes

Sustainability Implications:

• Varlen enables more sustainable AI research practices
• 2x training speed allows more experiments with same carbon budget
• Lower total compute hours reduce environmental impact
• Faster iteration cycles improve research productivity per watt

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Technical Specifications

Model Architecture and Objective

Architecture: Decoder-only Transformer (GPT-style)

Key Components:

• Multi-head causal self-attention (16 heads)
• SiLU-activated feed-forward networks
• Pre-layer normalization
• Learned positional embeddings
• Weight tying between input/output embeddings

Training Objective: Next-token prediction (causal language modeling)

Loss Function: Cross-entropy over vocabulary

Compute Infrastructure

Training Infrastructure:

• Cloud platform: Google Colab Pro+
• GPU: NVIDIA A100 (40GB)
• Framework: PyTorch 2.x
• Distributed: DDP with NCCL backend

Optimization Features:

• Mixed precision training (BF16)
• Gradient accumulation (8 or 14 steps, variant-dependent)
• Automatic gradient clipping
• Atomic checkpointing system
• Emergency recovery mechanisms

Software

Core Dependencies:

• PyTorch >= 2.0
• Transformers >= 4.30
• Flash-Attention >= 2.0 (Variant A only)
• SentencePiece >= 0.1.99

Training Framework:

• Custom curriculum learning pipeline
• Streaming JSONL dataset loader
• Phase-aware hyperparameter controller
• Fault-tolerant checkpoint manager

---

Citation

BibTeX:

@article{yildiz2026ozveri636m,
  title = {OZVERI-636M: Curriculum-Phased Training of Large Language Models ---
           A {\textasciitilde}4B-Token Controlled Study of Efficiency--Quality
           Trade-offs between FlashAttention v2 and Standard Attention},
  author = {Yildiz, Irfan},
  journal = {arXiv preprint},
  volume = {arXiv:XXXX.XXXXX},
  year = {2026},
  note = {Independent Research}
}

APA:

Yildiz, I. (2026). OZVERI-636M: Curriculum-phased training of large language models—A ~4B-token controlled study of efficiency–quality trade-offs between FlashAttention v2 and standard attention. arXiv. https://arxiv.org/abs/XXXX.XXXXX

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Model Card Authors

Irfan Yildiz - Independent Researcher

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Model Card Contact

For research inquiries and methodology questions:

• GitHub: [Repository - Training methodology and documentation]
• Email: irfan34yildiz@gmail.com
• Research Documentation: https://huggingface.co/irfantr/OZVERI-636M

Note: Model weights are not distributed. This page serves as research documentation for the training methodology, curriculum learning approach, and experimental findings.

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Glossary

• Curriculum Learning: Structured training where data is presented in phases of increasing complexity
• FlashAttention: Memory-efficient attention mechanism optimized for GPU architecture
• Varlen: Variable-length sequences without padding
• BPE: Byte Pair Encoding, a subword tokenization method
• Re-anchoring: Final training phase to prevent catastrophic forgetting
• Catastrophic Forgetting: Loss of previously learned information when learning new tasks
• Padding: Adding tokens to sequences to make them equal length (memory inefficient)
• Perplexity (PPL): Measure of prediction confidence (lower = better)
• Mean Loss: Average loss across all training steps

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Additional Information

Research Documentation Purpose

This model card serves as comprehensive documentation of:

• A 636M parameter Turkish-centric language model training methodology
• Phase-based curriculum learning implementation
• FlashAttention v2 varlen efficiency analysis
• Multi-phase training stability and convergence patterns
• Ablation studies on curriculum design choices
• Controlled comparison of varlen vs. padded attention mechanisms
• Detailed performance metrics including loss, perplexity, and efficiency

Model weights are intentionally not distributed. This documentation aims to contribute to:

• Research on low-resource language modeling
• Curriculum learning methodology development
• Training infrastructure optimization
• Memory-efficient attention mechanisms

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Comparative Studies

Completed Experiments:

• ✅ OZVERI-636M-Variant-A (Varlen): 49.42h, PPL 45.97, final loss 3.1088
• ✅ OZVERI-636M-Variant-B (Padded): 98.43h, PPL 54.37, final loss 2.9792

Controlled Variables:

• Identical training data and curriculum phases
• Same hyperparameter schedules (LR, weight decay, dropout, warmup)
• Equal token budget (~4.0B tokens)
• Identical model architecture (636M parameters)
• Same hardware (NVIDIA A100 40GB)

Measured Differences:

Dimension	Varlen	Padded	Result
Training time	49.42h	98.43h	-49.8%
Steps/hour	1,740	874	+99.2%
Mean loss	3.8280	3.9959	-4.2%
Final loss	3.1088	2.9792	+4.3%
Perplexity	45.97	54.37	-15.4%
GPU memory	7.17 GB	6.71 GB	+6.8%
Loss reduction	95.2%	95.7%	-0.5pp

Primary Findings:

1. Speed: Varlen achieves 2x training throughput
2. Time: 50% reduction in wall-clock training time
3. Mean Performance: Varlen has 4.2% better mean loss and 15.4% better perplexity
4. Final Quality: Padded has 4.3% better final loss
5. Memory: Padded uses 6.8% less GPU memory
6. Trade-off: Speed and mean performance (Varlen) vs. final quality (Padded)

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Implementation Details Available

Researchers can reference:

• Complete hyperparameter schedules
• Phase transition strategies
• Data preprocessing pipelines
• Tokenizer training methodology
• Checkpoint and recovery systems
• FlashAttention v2 integration patterns
• Varlen batching implementation
• Comparative performance metrics

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Framework Versions

• Transformers: 4.36.0
• PyTorch: 2.1.0
• Flash-Attention: 2.3.0
• SentencePiece: 0.1.99

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Acknowledgments

• FlashAttention v2 by Tri Dao et al.
• Google Colab Pro+ for computational resources
• PyTorch and Hugging Face communities

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License: Apache 2.0 (Research-Only Restriction)

Copyright © 2026 Irfan Yildiz

This model is provided for research and educational purposes only. Commercial use, redistribution of weights, or derivative commercial products require explicit written permission.

Last Updated: January 2026