267 lines
10 KiB
Markdown
267 lines
10 KiB
Markdown
---
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base_model: Qwen/Qwen3-1.7B
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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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pipeline_tag: text-generation
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library_name: transformers
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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-Qwen3-1.7B-TR
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[](https://opensource.org/licenses/Apache-2.0)
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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).
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**Resources:**
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- **Code:** [GitHub Repository](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-Qwen3-1.7B-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.
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**Key Features:**
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- Continual pre-training on approximately 225 billion tokens across four phases
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- Four-phase curriculum learning:
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- Phase 1: ~3.7B tokens
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- Phase 2: ~57B tokens
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- Phase 3: ~165B tokens
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- Phase 4: ~24.9B 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:** 1.7B
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**Base Model:** Qwen/Qwen3-1.7B
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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:** 28 transformer layers
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- **Hidden Size:** 2,048
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- **FFN Hidden Size:** 6,144
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- **Number of Heads:** 16
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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:** ~225 billion tokens (250,739,476,454 tokens across four phases)
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- **Training Method:** Four-phase curriculum learning
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- **Framework:** NVIDIA NeMo with Megatron-Core
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- **Hardware:** MareNostrum 5 supercomputer (BSC), H100 GPUs
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- **Precision:** BF16
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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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**Phase 1 (~3.7B tokens):**
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- Focus: Short, general-purpose Turkish texts
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- Purpose: Adapt model to Turkish language patterns while maintaining stability
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- Learning Rate: Higher with extended warmup
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- Dataset: Academic-focused data with semantic deduplication and FineWeb quality filtering
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**Phase 2 (~57B tokens):**
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- Focus: Legal content with domain-specific terminology
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- Includes: Court decisions, legal articles, regulatory documents
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- Data Replay: YÖKTEZ academic legal data from Phase 1
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- Dataset: Lighter pipeline with FineWeb quality filtering, preserving topical diversity
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**Phase 3 (~165B tokens):**
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- Focus: Long, structurally complex normative texts
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- Includes: Full court decisions, legislative documents, academic legal theses
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- Purpose: Refine model's understanding of legal reasoning patterns
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- Dataset: Long-form documents with merged consecutive pages
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**Phase 4 (~24.9B tokens):**
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- Focus: Extended domain-specific refinement
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- Includes: Mixed complexity documents
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- Purpose: Consolidate knowledge and improve generalization
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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: Phase-dependent (200-2,340 steps)
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- Precision: BF16 mixed precision
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- Framework: NVIDIA NeMo with Megatron-Core
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**Hardware Infrastructure:**
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- **System:** MareNostrum 5 ACC partition at Barcelona Supercomputing Center (BSC)
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- **Node Configuration:** Each node equipped with 4× NVIDIA Hopper H100 64GB GPUs (SXM), 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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- **Phase-Specific Hardware:**
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- **Phase 1:** 50 nodes, 200 GPUs, ~3.7B tokens, 3.77M tokens/sec throughput, 20.7% median MFU
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- **Phase 2:** 50 nodes, 200 GPUs, ~57B tokens, 3.59M tokens/sec throughput, 20.7% median MFU
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- **Phase 3:** 100 nodes, 400 GPUs, ~165B tokens, 7.35M tokens/sec throughput, 20.3% median MFU
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- **Phase 4:** 50 nodes, 200 GPUs, ~24.9B tokens, 3.25M tokens/sec throughput, 20.6% median MFU
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**Catastrophic Forgetting Mitigation:**
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- Curriculum learning: Progressive transition from general to specialized knowledge
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- Replay buffer: YÖKTEZ data from Phase 1 included in Phase 2
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- Conservative learning rates and extended warmup periods
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**Performance:** Achieved 36.2% perplexity reduction on Turkish legal text compared to base Qwen3-1.7B model.
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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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*Qwen3-1.7B CPT Dataset Distribution across Four Phases. The curriculum learning strategy progressively introduces more complex legal content.*
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*Qwen3-1.7B CPT Training and Validation Loss Across Four Phases. The model shows consistent improvement throughout all training phases.*
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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 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.*
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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-Qwen3-1.7B-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-Qwen3-1.7B-TR")
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model = AutoModelForCausalLM.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-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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### Chat Format
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
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model = AutoModelForCausalLM.from_pretrained("newmindai/Mecellem-Qwen3-1.7B-TR")
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messages = [
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{"role": "user", "content": "Türk hukuk sisteminde sözleşme feshi nasıl yapılır?"}
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]
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# Apply chat template
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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# Generate response
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=256)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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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 and Sağbaş, Ömer Can},
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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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``` |