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Model: thangvip/qwen3-4b-vietnamese-legal-grpo Source: Original Platform
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README.md
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README.md
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---
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base_model: thangvip/qwen3-4b-legal-pretrain-synthetic-8k
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library_name: transformers
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model_name: thangvip/qwen3-4b-vietnamese-legal-grpo
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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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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
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pipeline_tag: text-generation
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widget:
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- example_title: "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 Fine-tuned
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## 🏛️ Model Description
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This model is a **Vietnamese legal reasoning specialist** fine-tuned using **Group Relative Policy Optimization (GRPO)** on Vietnamese legal question-answering data. It's specifically designed to perform **syllogistic reasoning** for Vietnamese legal scenarios.
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### 🎯 Base Model
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- **Base**: [thangvip/qwen3-4b-legal-pretrain-synthetic-8k](https://huggingface.co/thangvip/qwen3-4b-legal-pretrain-synthetic-8k)
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- **Architecture**: Qwen 3 (4B parameters)
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- **Language**: Vietnamese
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- **Specialization**: Legal reasoning and syllogism
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### 🔥 Key Features
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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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## 📊 Model Architecture
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- **Parameters**: ~4B
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- **Vocabulary Size**: 151936
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- **Hidden Size**: 2560
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- **Layers**: 36
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- **Attention Heads**: 32
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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/qwen3-4b-vietnamese-legal-grpo"
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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=1024,
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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/qwen3-4b-vietnamese-legal-grpo",
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tokenizer="thangvip/qwen3-4b-vietnamese-legal-grpo",
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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/qwen3-4b-legal-pretrain-synthetic-8k
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- **Training Steps**: N/A
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- **Learning Rate**: N/A
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- **Batch Size**: N/A
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### Training Data
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- **Domain**: Vietnamese legal question-answering
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- **Format**: Syllogistic reasoning pairs
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- **Structure**: Question → Structured legal reasoning response
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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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- **Legal Education**: Teaching legal reasoning methodology
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- **Legal Research**: Preliminary analysis of legal questions
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- **Document Drafting**: Structured legal argument generation
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- **Legal Consultation**: Initial legal guidance (with human review)
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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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- **Context Window**: Limited by base model's context length
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## 📄 Citation
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If you use this model, please cite:
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|
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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/qwen3-4b-vietnamese-legal-grpo}
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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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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
|
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
|
||||
{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
|
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{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
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{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
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{{- '\n' }}
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{%- endif %}
|
||||
{%- if tool_call.function %}
|
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{%- set tool_call = tool_call.function %}
|
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{%- endif %}
|
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{{- '<tool_call>\n{"name": "' }}
|
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{{- tool_call.name }}
|
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{{- '", "arguments": ' }}
|
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{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
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{%- else %}
|
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{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
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{%- endfor %}
|
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{%- endif %}
|
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{{- '<|im_end|>\n' }}
|
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{%- elif message.role == "tool" %}
|
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
|
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{{- content }}
|
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{{- '\n</tool_response>' }}
|
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
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{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
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{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
|
||||
{%- endif %}
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config.json
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config.json
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{
|
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"architectures": [
|
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"Qwen3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
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"hidden_size": 2560,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 9728,
|
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"layer_types": [
|
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"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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||||
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|
||||
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||||
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||||
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|
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151647": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
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|
||||
"151649": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151650": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151659": {
|
||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
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|
||||
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|
||||
"151660": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151663": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151664": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151666": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
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|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
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|
||||
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|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
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|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5ac5f6a66492f95a3fc048fcca1f3cd6ab9f3ceff7fe614fdf8d6c34da39df76
|
||||
size 7057
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user