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Model: newmindai/Mecellem-Qwen3-1.7B-TR Source: Original Platform
2026-04-12 07:45:01 +08:00
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+---
+base_model: Qwen/Qwen3-1.7B
+language:
+- tr
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation
+- turkish
+- legal
+- turkish-legal
+- mecellem
+- qwen
+- decoder-only
+- continual-pretraining
+- TRUBA
+- MN5
+---
+
+# Mecellem-Qwen3-1.7B-TR
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+
+Mecellem-Qwen3-1.7B-TR is a Turkish legal language model presented in [Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain](https://huggingface.co/papers/2601.16018).
+
+**Resources:**
+- **Code:** [GitHub Repository](https://github.com/newmindai/mecellem-models)
+- **Paper:** [arXiv:2601.16018](https://arxiv.org/abs/2601.16018)
+
+## Model Description
+
+Mecellem-Qwen3-1.7B-TR is a Turkish legal language model adapted through Continual Pre-training (CPT) on Turkish legal and official texts. The model is based on Qwen3-1.7B decoder architecture (1.7B parameters) and trained using a four-phase curriculum learning strategy specifically designed to account for Turkish linguistic complexity. The CPT process progressively transitions from general-purpose texts to domain-specific legal content, achieving 36.2% perplexity reduction on Turkish legal text compared to the base Qwen3-1.7B model.
+
+**Key Features:**
+- Continual pre-training on approximately 225 billion tokens across four phases
+- Four-phase curriculum learning:
+  - Phase 1: ~3.7B tokens
+  - Phase 2: ~57B tokens
+  - Phase 3: ~165B tokens
+  - Phase 4: ~24.9B tokens
+- Dataset includes Turkish legal sources (Yargıtay, Danıştay, YÖKTEZ) and general Turkish web data (FineWeb2, CulturaX)
+- Preserves general language capabilities while injecting domain-specific legal knowledge
+
+**Model Type:** Decoder-only Language Model  
+**Parameters:** 1.7B  
+**Base Model:** Qwen/Qwen3-1.7B  
+**Architecture:** Qwen3 decoder with grouped query attention (GQA)
+
+### Architecture Details
+
+- **Max Position Embeddings:** 40,960 tokens
+- **Number of Layers:** 28 transformer layers
+- **Hidden Size:** 2,048
+- **FFN Hidden Size:** 6,144
+- **Number of Heads:** 16
+- **Number of KV Heads (GQA):** 8
+- **Activation Function:** SwiGLU
+- **Position Encodings:** RoPE (Rotary Position Embeddings)
+- **Layer Norm:** RMSNorm
+
+### Training Details
+
+**Continual Pre-training (CPT):**
+- **Total Training Tokens:** ~225 billion tokens (250,739,476,454 tokens across four phases)
+- **Training Method:** Four-phase curriculum learning
+- **Framework:** NVIDIA NeMo with Megatron-Core
+- **Hardware:** MareNostrum 5 supercomputer (BSC), H100 GPUs
+- **Precision:** BF16
+
+**Dataset Composition:**
+- **Legal Sources:**
+  - Court of Cassation (Yargıtay): 10.3M sequences, ~3.43B tokens
+  - Council of State (Danıştay): 151K sequences, ~0.11B tokens
+  - Academic theses (YÖKTEZ): 21.1M sequences, ~9.61B tokens (after DocsOCR processing)
+- **General Turkish Sources:**
+  - FineWeb2: General Turkish web data
+  - CulturaX: Multilingual corpus (Turkish subset)
+  - Total general Turkish: 212M sequences, ~96.17B tokens
+- **Additional Categories:** English, Mathematics, Python code, multilingual content (Spanish, Arabic, Russian, Chinese)
+
+**Phase 1 (~3.7B tokens):**
+- Focus: Short, general-purpose Turkish texts
+- Purpose: Adapt model to Turkish language patterns while maintaining stability
+- Learning Rate: Higher with extended warmup
+- Dataset: Academic-focused data with semantic deduplication and FineWeb quality filtering
+
+**Phase 2 (~57B tokens):**
+- Focus: Legal content with domain-specific terminology
+- Includes: Court decisions, legal articles, regulatory documents
+- Data Replay: YÖKTEZ academic legal data from Phase 1
+- Dataset: Lighter pipeline with FineWeb quality filtering, preserving topical diversity
+
+**Phase 3 (~165B tokens):**
+- Focus: Long, structurally complex normative texts
+- Includes: Full court decisions, legislative documents, academic legal theses
+- Purpose: Refine model's understanding of legal reasoning patterns
+- Dataset: Long-form documents with merged consecutive pages
+
+**Phase 4 (~24.9B tokens):**
+- Focus: Extended domain-specific refinement
+- Includes: Mixed complexity documents
+- Purpose: Consolidate knowledge and improve generalization
+
+**Training Hyperparameters:**
+- Sequence Length: 4,096 tokens
+- Optimizer: Adam with cosine learning rate schedule
+- Max Learning Rate: 5×10⁻⁵
+- Min Learning Rate: 5×10⁻⁶
+- Weight Decay: 0.01
+- Warmup Steps: Phase-dependent (200-2,340 steps)
+- Precision: BF16 mixed precision
+- Framework: NVIDIA NeMo with Megatron-Core
+
+**Hardware Infrastructure:**
+- **System:** MareNostrum 5 ACC partition at Barcelona Supercomputing Center (BSC)
+- **Node Configuration:** Each node equipped with 4× NVIDIA Hopper H100 64GB GPUs (SXM), 80 CPU cores, 512GB DDR5 memory
+- **Interconnect:** 800 Gb/s InfiniBand for distributed training
+- **GPU Interconnect:** NVLink for intra-node GPU communication (4 GPUs per node connected via NVLink)
+- **Distributed Training:** Data-parallel multi-node and multi-GPU distributed architecture with 4 GPUs per node
+- **InfiniBand Network:** Enabled efficient processing of large-scale token flow and ensured high scalability and training stability in long-term CPT training
+- **Phase-Specific Hardware:**
+  - **Phase 1:** 50 nodes, 200 GPUs, ~3.7B tokens, 3.77M tokens/sec throughput, 20.7% median MFU
+  - **Phase 2:** 50 nodes, 200 GPUs, ~57B tokens, 3.59M tokens/sec throughput, 20.7% median MFU
+  - **Phase 3:** 100 nodes, 400 GPUs, ~165B tokens, 7.35M tokens/sec throughput, 20.3% median MFU
+  - **Phase 4:** 50 nodes, 200 GPUs, ~24.9B tokens, 3.25M tokens/sec throughput, 20.6% median MFU
+
+**Catastrophic Forgetting Mitigation:**
+- Curriculum learning: Progressive transition from general to specialized knowledge
+- Replay buffer: YÖKTEZ data from Phase 1 included in Phase 2
+- Conservative learning rates and extended warmup periods
+
+**Performance:** Achieved 36.2% perplexity reduction on Turkish legal text compared to base Qwen3-1.7B model.
+
+### Training Visualization
+
+The following visualizations show the model's training progress and dataset distribution:
+
+![Dataset Distribution](qwen3-1.7_dataset.png)
+
+*Qwen3-1.7B CPT Dataset Distribution across Four Phases. The curriculum learning strategy progressively introduces more complex legal content.*
+
+![Training Loss](qwen3-1.7b_loss.png)
+
+*Qwen3-1.7B CPT Training and Validation Loss Across Four Phases. The model shows consistent improvement throughout all training phases.*
+
+### Benchmark Performance
+
+The model was evaluated using the Muhakim reward model on Turkish legal tasks:
+
+![Benchmark Performance](1_7b_qwen_armo.png)
+
+*Benchmark Performance of 1.7B Decoder-Only Models Across Context Lengths Using the Muhakim Reward Model. Mecellem-Qwen3-1.7B-TR consistently outperforms the base Qwen3-1.7B model across all five legal quality objectives, with particularly pronounced gains for depth of coverage, statute reference usage, and legal accuracy.*
+
+### Rewards Comparison Analysis
+
+The following visualization compares rewards across different token lengths for base vs CPT models:
+
+![Rewards Comparison](comparison_rewards_by_token_length-filtered.png)
+
+*Rewards Comparison: Base vs CPT Models Across Token Lengths. Mecellem-Qwen3-1.7B-TR shows consistent improvements over the base model across all context length settings, demonstrating the effectiveness of Turkish legal domain adaptation.*
+
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Text Generation
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
+model = AutoModelForCausalLM.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
+
+# Example prompt
+prompt = "Türk hukuk sisteminde sözleşme feshi"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.9
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+### Chat Format
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
+model = AutoModelForCausalLM.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
+
+messages = [
+    {"role": "user", "content": "Türk hukuk sisteminde sözleşme feshi nasıl yapılır?"}
+]
+
+# Apply chat template
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+
+# Generate response
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=256)
+    
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Use Cases
+
+- Turkish legal text generation
+- Legal document summarization
+- Legal question answering
+- Legal text completion
+- Domain-specific language modeling for Turkish legal domain
+- Retrieval-Augmented Generation (RAG) applications
+
+## Acknowledgments
+
+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.
+
+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.
+
+## References
+
+If you use this model, please cite our paper:
+
+```bibtex
+@article{mecellem2026,
+  title={Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain},
+  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 and Sağbaş, Ömer Can},
+  journal={arXiv preprint arXiv:2601.16018},
+  year={2026},
+  month={January},
+  url={https://arxiv.org/abs/2601.16018},
+  doi={10.48550/arXiv.2601.16018},
+  eprint={2601.16018},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL}
+}
+```
+### Base Model References
+
+```bibtex
+@article{qwen2024,
+  title={Qwen3: A Large Language Model Series},
+  author={Qwen Team},
+  journal={arXiv preprint arXiv:2409.00000},
+  year={2024}
+}
+```