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Model: newmindai/Mecellem-Qwen3-4B-TR Source: Original Platform
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README.md
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---
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base_model: Qwen/Qwen3-4B
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language:
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- tr
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- turkish
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- legal
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- turkish-legal
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- mecellem
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- qwen
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- decoder-only
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- continual-pretraining
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- TRUBA
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- MN5
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---
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# Mecellem-Qwen3-4B-TR
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[](https://opensource.org/licenses/Apache-2.0)
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This repository contains the **Mecellem-Qwen3-4B-TR** model, as presented in the paper [Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain](https://huggingface.co/papers/2601.16018).
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- **GitHub Repository:** [newmindai/mecellem-models](https://github.com/newmindai/mecellem-models)
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- **Paper:** [arXiv:2601.16018](https://arxiv.org/abs/2601.16018)
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## Model Description
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Mecellem-Qwen3-4B-TR is a Turkish legal language model adapted through Continual Pre-training (CPT) on Turkish legal and official texts. The model is based on Qwen3-4B decoder architecture (4B parameters) and trained using a single-phase, large-scale CPT process. Unlike the 1.7B model's four-phase curriculum learning, this model employs a single-phase training strategy on a comprehensive dataset, demonstrating that larger model capacity can benefit from direct large-scale domain adaptation.
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**Key Features:**
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- Continual pre-training on approximately 270.8 billion tokens in a single phase
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- Single-phase large-scale CPT process (270,791,712,595 tokens)
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- Dataset includes Turkish legal sources (Yargıtay, Danıştay, YÖKTEZ) and general Turkish web data (FineWeb2, CulturaX)
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- Preserves general language capabilities while injecting domain-specific legal knowledge
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**Model Type:** Decoder-only Language Model
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**Parameters:** 4B
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**Base Model:** Qwen/Qwen3-4B
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**Architecture:** Qwen3 decoder with grouped query attention (GQA)
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### Architecture Details
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- **Max Position Embeddings:** 40,960 tokens
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- **Number of Layers:** 36 transformer layers
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- **Hidden Size:** 2,560
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- **FFN Hidden Size:** 9,728
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- **Number of Heads:** 32
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- **Number of KV Heads (GQA):** 8
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- **Activation Function:** SwiGLU
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- **Position Encodings:** RoPE (Rotary Position Embeddings)
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- **Layer Norm:** RMSNorm
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### Training Details
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**Continual Pre-training (CPT):**
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- **Total Training Tokens:** ~270.8 billion tokens (270,791,712,595 tokens)
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- **Training Method:** Single-phase large-scale CPT
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- **Framework:** NVIDIA NeMo with Megatron-Core
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- **Precision:** BF16 mixed precision
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- **Hardware Infrastructure:**
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- **System:** MareNostrum 5 ACC partition at Barcelona Supercomputing Center (BSC)
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- **Compute Nodes:** 100 nodes
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- **GPUs:** 400× NVIDIA Hopper H100 64GB GPUs (SXM) (4 GPUs per node)
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- **Node Configuration:** Each node equipped with 4× H100 GPUs, 80 CPU cores, 512GB DDR5 memory
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- **Interconnect:** 800 Gb/s InfiniBand for distributed training
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- **GPU Interconnect:** NVLink for intra-node GPU communication (4 GPUs per node connected via NVLink)
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- **Distributed Training:** Data-parallel multi-node and multi-GPU distributed architecture with 4 GPUs per node
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- **InfiniBand Network:** Enabled efficient processing of large-scale token flow and ensured high scalability and training stability in long-term CPT training
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- **Hardware Utilization:** 18.7% median MFU, 2.57M tokens/sec throughput
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**Dataset Composition:**
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- **Legal Sources:**
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- Court of Cassation (Yargıtay): 10.3M sequences, ~3.43B tokens
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- Council of State (Danıştay): 151K sequences, ~0.11B tokens
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- Academic theses (YÖKTEZ): 21.1M sequences, ~9.61B tokens (after DocsOCR processing)
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- **General Turkish Sources:**
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- FineWeb2: General Turkish web data
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- CulturaX: Multilingual corpus (Turkish subset)
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- Total general Turkish: 212M sequences, ~96.17B tokens
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- **Additional Categories:** English, Mathematics, Python code, multilingual content (Spanish, Arabic, Russian, Chinese)
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**Training Hyperparameters:**
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- Sequence Length: 4,096 tokens
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- Optimizer: Adam with cosine learning rate schedule
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- Max Learning Rate: 5×10⁻⁵
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- Min Learning Rate: 5×10⁻⁶
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- Weight Decay: 0.01
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- Warmup Steps: 7,675 steps
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- Max Steps: 153,508 steps
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- Global Batch Size: 400
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- Per-GPU Batch Size: 1
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- Gradient Accumulation: 16
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### Training Visualization
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The following visualizations show the model's training progress and dataset distribution:
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*Qwen3-4B CPT Dataset Distribution Single Phase. The model was trained using a single-phase, large-scale CPT process.*
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*Qwen3-4B CPT Training and Validation Loss Curves. The model shows consistent improvement throughout training.*
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### Benchmark Performance
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The model was evaluated using the Muhakim reward model on Turkish legal tasks:
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*Benchmark Performance of 4B Decoder-Only Models Across Context Lengths Using the Muhakim Reward Model. Mecellem-Qwen3-4B-TR consistently outperforms the base Qwen3-4B model across all five legal quality objectives.*
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### Rewards Comparison Analysis
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The following visualization compares rewards across different token lengths for base vs CPT models:
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*Rewards Comparison: Base vs CPT Models Across Token Lengths. Mecellem-Qwen3-4B-TR shows consistent improvements over the base model across all context length settings, demonstrating the effectiveness of Turkish legal domain adaptation.*
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## Usage
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### Installation
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```bash
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pip install transformers torch
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```
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### Text Generation
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("newmindai/Mecellem-Qwen3-4B-TR")
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model = AutoModelForCausalLM.from_pretrained("newmindai/Mecellem-Qwen3-4B-TR")
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# Example prompt
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prompt = "Türk hukuk sisteminde sözleşme feshi"
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.7,
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do_sample=True,
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top_p=0.9
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)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(generated_text)
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```
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## Use Cases
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- Turkish legal text generation
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- Legal document summarization
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- Legal question answering
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- Legal text completion
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- Domain-specific language modeling for Turkish legal domain
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- Retrieval-Augmented Generation (RAG) applications
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## Acknowledgments
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This work was supported by the EuroHPC Joint Undertaking through project etur46 with access to the MareNostrum 5 supercomputer, hosted by Barcelona Supercomputing Center (BSC), Spain. MareNostrum 5 is owned by EuroHPC JU and operated by BSC. We are grateful to the BSC support team for their assistance with job scheduling, environment configuration, and technical guidance throughout the project.
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The numerical calculations reported in this work were fully/partially performed at TÜBİTAK ULAKBİM, High Performance and Grid Computing Center (TRUBA resources). The authors gratefully acknowledge the know-how provided by the MINERVA Support for expert guidance and collaboration opportunities in HPC-AI integration.
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## References
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If you use this model, please cite our paper:
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```bibtex
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@article{mecellem2026,
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title={Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain},
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author={Uğur, Özgür and Göksu, Mahmut and Çimen, Mahmut and Yılmaz, Musa and Şavirdi, Esra and Demir, Alp Talha and Güllüce, Rumeysa and İclal Çetin, Ömer Can Sağbaş},
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journal={arXiv preprint arXiv:2601.16018},
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year={2026},
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month={January},
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url={https://arxiv.org/abs/2601.16018},
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doi={10.48550/arXiv.2601.16018},
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eprint={2601.16018},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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### Base Model References
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```bibtex
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@article{qwen2024,
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title={Qwen3: A Large Language Model Series},
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author={Qwen Team},
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journal={arXiv preprint arXiv:2409.00000},
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year={2024}
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}
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```
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