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Model: thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2 Source: Original Platform
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README.md
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README.md
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base_model: thangvip/qwen3-1.7b-vietnamese-legal-grpo
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library_name: transformers
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model_name: thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2
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tags:
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- grpo
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- vietnamese
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- legal
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- reasoning
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- syllogism
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- trl
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- generated_from_trainer
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- phase2
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- hard-difficulty
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license: apache-2.0
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language:
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- vi
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datasets:
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- legal-qa-vietnamese-hard
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pipeline_tag: text-generation
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widget:
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- example_title: "Hard Legal Question Example"
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text: "Câu hỏi: Một công ty có nghĩa vụ gì khi sa thải nhân viên do tái cơ cấu?"
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---
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# Vietnamese Legal Reasoning Model - GRPO Phase 2 (Hard Difficulty)
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## 🏛️ Model Description
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This is a **Phase 2 Vietnamese legal reasoning specialist** fine-tuned using **Group Relative Policy Optimization (GRPO)** on **hard difficulty** Vietnamese legal question-answering data. This model builds upon the Phase 1 training and is specifically designed to handle more complex **syllogistic reasoning** for challenging Vietnamese legal scenarios.
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### 🎯 Base Model
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- **Base**: [thangvip/qwen3-1.7b-vietnamese-legal-grpo](https://huggingface.co/thangvip/qwen3-1.7b-vietnamese-legal-grpo)
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- **Architecture**: Qwen 3 (1.7B parameters)
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- **Language**: Vietnamese
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- **Specialization**: Advanced legal reasoning and syllogism (Hard difficulty)
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- **Training Phase**: Phase 2 - Hard Level QA
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### 🔥 Key Features
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✅ **Phase 2 Training**: Advanced model trained on hard difficulty legal questions
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✅ **Syllogistic Reasoning**: Structured legal arguments (Major Premise → Minor Premise → Conclusion)
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✅ **Vietnamese Legal Domain**: Trained on Vietnamese legal texts and Q&A
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✅ **GRPO Optimization**: Advanced policy optimization for better reasoning
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✅ **Citation Support**: Generates responses with legal citations
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✅ **Structured Output**: Uses XML-like tags for organized responses
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✅ **Extended Context**: Supports up to 8192 tokens for complex reasoning chains
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## 📊 Model Architecture
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- **Parameters**: ~1.7B
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- **Vocabulary Size**: 151936
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- **Hidden Size**: 2048
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- **Layers**: 28
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- **Attention Heads**: 16
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- **Max Completion Length**: 8192 tokens (extended for complex reasoning)
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## 🚀 Quick Start
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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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### Basic Usage
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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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model_name = "thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Format your legal question
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system_prompt = """Bạn là một chuyên gia pháp lý. Hãy trả lời câu hỏi bằng cách sử dụng phương pháp lập luận tam đoạn luận (syllogism).
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Trước tiên, hãy suy nghĩ về vấn đề trong thẻ <think></think>.
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Sau đó, trả lời theo định dạng sau:
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<answer>
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<major_premise>[Quy định pháp luật chung]</major_premise>
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<minor_premise>[Sự kiện cụ thể trong câu hỏi]</minor_premise>
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<conclusion>[Áp dụng quy định vào sự kiện để đưa ra kết luận]</conclusion>
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</answer>
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Hãy đảm bảo trích dẫn chính xác các điều luật liên quan."""
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question = "Một công ty có nghĩa vụ gì khi sa thải nhân viên do tái cơ cấu?"
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# Create conversation
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": question}
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]
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# Generate response
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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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=2048, # Extended for hard difficulty complex reasoning
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### Pipeline Usage
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```python
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from transformers import pipeline
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# Create text generation pipeline
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generator = pipeline(
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"text-generation",
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model="thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2",
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tokenizer="thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Generate legal reasoning
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prompt = "Câu hỏi: Quyền và nghĩa vụ của người thuê nhà khi hợp đồng thuê hết hạn?"
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result = generator(prompt, max_new_tokens=512, temperature=0.7)
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print(result[0]['generated_text'])
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```
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## 🎯 Training Details
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### Training Procedure
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- **Method**: Group Relative Policy Optimization (GRPO)
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- **Base Model**: thangvip/qwen3-1.7b-vietnamese-legal-grpo
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- **Training Phase**: Phase 2 - Hard Difficulty
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- **Training Steps**: N/A (typically 1500 steps)
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- **Learning Rate**: N/A
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- **Batch Size**: N/A
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- **Max Completion Length**: 8192 tokens (doubled for complex reasoning)
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### Training Data
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- **Domain**: Vietnamese legal question-answering (Hard difficulty)
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- **Format**: Syllogistic reasoning pairs
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- **Structure**: Question → Structured legal reasoning response
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- **Difficulty Level**: Hard - Complex multi-step legal reasoning scenarios
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- **Dataset**: `hard_level_qa.jsonl`
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### Two-Phase Training Approach
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1. **Phase 1**: Initial GRPO training on normal difficulty questions
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- Base model: `thangvip/qwen3-4b-legal-pretrain-synthetic-8k` or similar
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- Dataset: `normal_level_qa.jsonl`
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- Max completion: 4096 tokens
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2. **Phase 2** (This Model): Continued training on hard difficulty questions with extended context window
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- Base model: Phase 1 trained model (`thangvip/qwen3-1.7b-vietnamese-legal-grpo`)
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- Dataset: `hard_level_qa.jsonl`
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- Max completion: 8192 tokens (doubled for complex reasoning)
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- Training steps: ~1500 steps
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This progressive training approach allows the model to first master basic legal reasoning before tackling more complex, multi-step legal problems.
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### Reward System
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The model was trained with a sophisticated reward system:
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- **Correctness** (35%): Factual accuracy against reference answers
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- **Format Compliance** (20%): Proper use of syllogistic structure
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- **Citation Accuracy** (15%): Relevant and accurate legal citations
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- **Reasoning Quality** (15%): Quality of legal reasoning process
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- **Hallucination Penalty** (10%): Penalty for unsupported claims
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- **Length Penalty** (5%): Penalty for exceeding maximum token length
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## 📝 Expected Output Format
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The model generates structured responses in this format:
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```xml
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<think>
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[Internal reasoning about the legal question]
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</think>
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<answer>
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<major_premise>
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[General legal rule or principle applicable to the situation]
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</major_premise>
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<minor_premise>
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[Specific facts from the question that relate to the legal rule]
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</minor_premise>
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<conclusion>
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[Legal conclusion that follows logically from applying the rule to the facts]
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</conclusion>
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</answer>
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```
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## 🎯 Use Cases
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- **Complex Legal Education**: Teaching advanced legal reasoning methodology for difficult cases
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- **Advanced Legal Research**: Preliminary analysis of complex legal questions
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- **Multi-step Legal Analysis**: Structured legal argument generation for intricate scenarios
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- **Legal Consultation**: Initial legal guidance for challenging cases (with human review)
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- **Legal Training**: Demonstrating proper syllogistic reasoning for complex legal problems
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## ⚠️ Limitations
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- **Domain Specific**: Optimized for Vietnamese legal context
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- **Educational Purpose**: Should not replace professional legal advice
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- **Fact Checking Required**: Always verify legal citations and conclusions
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- **Extended Generation**: May produce lengthy responses (up to 8192 tokens) for complex questions
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- **Phase 2 Training**: Built upon Phase 1 model; requires understanding of base model capabilities
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## 📄 Citation
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If you use this model, please cite:
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```bibtex
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@misc{vietnamese-legal-grpo-2024,
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title={Vietnamese Legal Reasoning Model with GRPO},
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author={Your Name},
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year={2024},
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publisher={Hugging Face},
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url={https://huggingface.co/thangvip/qwen3-1.7b-vietnamese-legal-grpo-phase-2}
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}
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```
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## 🤝 Contributing
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Contributions are welcome! Please see our [contributing guidelines](CONTRIBUTING.md).
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## 📜 License
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This model is released under the Apache 2.0 License.
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## 🙏 Acknowledgments
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- **TRL Team**: For the GRPO implementation
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- **Qwen Team**: For the excellent base model
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- **Hugging Face**: For the transformers library and model hosting
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---
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**Note**: This model is for educational and research purposes. Always consult qualified legal professionals for actual legal advice.
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