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| 1 |
+
---
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| 2 |
+
language: th
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| 3 |
+
tags:
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| 4 |
+
- gpt2
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| 5 |
+
- thai
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| 6 |
+
- reasoning
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| 7 |
+
- thinking
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| 8 |
+
- instruction-tuning
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| 9 |
+
- conversational-ai
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| 10 |
+
license: apache-2.0
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| 11 |
+
datasets:
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| 12 |
+
- HelpingAI/Intermediate-Thinking-130k
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| 13 |
+
- custom-thai-dataset
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| 14 |
+
---
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| 15 |
+
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| 16 |
+
# GPT-Thai-Think 🤖
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| 17 |
+
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| 18 |
+
A Thai-centric GPT-2 model with **thinking and reasoning capabilities**! This model can engage in conversations and solve problems while showing its step-by-step reasoning process.
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| 19 |
+
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| 20 |
+
## 🌟 Key Features
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| 21 |
+
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| 22 |
+
- **Thai Language Support**: Optimized for Thai text processing
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| 23 |
+
- **Thinking Capabilities**: Shows step-by-step reasoning with `<think>` tags
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| 24 |
+
- **Conversational AI**: Handles instruction-response conversations
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| 25 |
+
- **Mathematical Reasoning**: Solves math problems with calculation steps
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| 26 |
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- **Bilingual**: Supports both Thai and English
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| 27 |
+
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| 28 |
+
## 📊 Model Details
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| 29 |
+
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| 30 |
+
- **Model Size**: 8.1M parameters
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| 31 |
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- **Architecture**: GPT-2 with 4 layers, 256 embedding dimension
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| 32 |
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- **Vocabulary**: 19,000 tokens (Thai-optimized)
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| 33 |
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- **Training Data**:
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| 34 |
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- 10,029 Thai conversation pairs
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| 35 |
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- 25,000 reasoning examples from HelpingAI dataset
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| 36 |
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- **Training**: 3 epochs with final loss 2.4
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| 37 |
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| 38 |
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## 🚀 Usage
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| 39 |
+
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| 40 |
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### Basic Usage
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| 41 |
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```python
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| 42 |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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| 43 |
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model = GPT2LMHeadModel.from_pretrained("your-username/gpt-thai-think")
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| 45 |
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tokenizer = GPT2Tokenizer.from_pretrained("your-username/gpt-thai-think")
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| 46 |
+
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| 47 |
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# Generate text
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| 48 |
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prompt = "คำสั่ง: สวัสดีครับ
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| 49 |
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คำตอบ:"
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| 50 |
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inputs = tokenizer(prompt, return_tensors="pt")
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| 51 |
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outputs = model.generate(**inputs, max_length=100)
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| 52 |
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response = tokenizer.decode(outputs[0])
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| 53 |
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```
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| 54 |
+
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| 55 |
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### With Thinking
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| 56 |
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```python
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| 57 |
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# The model shows reasoning steps
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| 58 |
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prompt = "Question: What is 15% of 200?
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| 59 |
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"
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| 60 |
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inputs = tokenizer(prompt, return_tensors="pt")
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| 61 |
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outputs = model.generate(**inputs, max_length=150)
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| 62 |
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response = tokenizer.decode(outputs[0])
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| 63 |
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# Response includes <think> tags with reasoning steps
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| 64 |
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```
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| 65 |
+
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| 66 |
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## 💡 Example Outputs
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| 67 |
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| 68 |
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### Math Problem
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| 69 |
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```
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| 70 |
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Question: If a train travels 120 km in 2 hours, what is its average speed?
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| 71 |
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<think>
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| 72 |
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To find average speed, I need to divide distance by time.
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| 73 |
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Distance = 120 km, Time = 2 hours
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| 74 |
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Speed = Distance ÷ Time = 120 km ÷ 2 hours = 60 km/h
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| 75 |
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</think>
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| 76 |
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Final Answer: 60 km/h
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| 77 |
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```
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| 78 |
+
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| 79 |
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### Thai Conversation
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| 80 |
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```
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| 81 |
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คำสั่ง: สวัสดีครับ
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| 82 |
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คำตอบ: สวัสดีครับ ยินดีที่ได้คุยกันครับ
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| 83 |
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```
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| 84 |
+
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| 85 |
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## 🏗️ Architecture
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| 86 |
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| 87 |
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- **Base Model**: GPT-2 Small
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| 88 |
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- **Embeddings**: 256 dimensions
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| 89 |
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- **Layers**: 4 transformer blocks
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| 90 |
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- **Attention Heads**: 4
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| 91 |
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- **Vocabulary**: SentencePiece with Thai optimization
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| 92 |
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- **Special Tokens**:
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| 93 |
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- `<s>`: Start of sequence
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| 94 |
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- `</s>`: End of sequence
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| 95 |
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- `<pad>`: Padding token
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| 96 |
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- `<unk>`: Unknown token
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| 97 |
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- `<mask>`: Mask token
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| 98 |
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| 99 |
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## 📚 Training Data
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| 100 |
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| 101 |
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### Primary Datasets
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| 102 |
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1. **Thai Conversations**: 10,029 instruction-response pairs
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| 103 |
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2. **Reasoning Examples**: 25,000 problems with step-by-step solutions
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| 104 |
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3. **HelpingAI Dataset**: Intermediate thinking patterns
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| 105 |
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| 106 |
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### Data Format
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| 107 |
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```
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คำสั่ง: [instruction]
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| 109 |
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คำตอบ: [response with optional <think> reasoning]
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| 110 |
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```
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| 111 |
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| 112 |
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## 🎯 Capabilities
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| 113 |
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| 114 |
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✅ **Conversational Thai**: Natural Thai conversations
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| 115 |
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✅ **Mathematical Reasoning**: Step-by-step calculations
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| 116 |
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✅ **Logic Problems**: Deductive reasoning
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| 117 |
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✅ **Word Problems**: Problem decomposition
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| 118 |
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✅ **Instruction Following**: Structured responses
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| 119 |
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✅ **Thinking Process**: Visible reasoning steps
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| 120 |
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| 121 |
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## 🔧 Technical Specifications
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| 122 |
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| 123 |
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- **Framework**: PyTorch + Transformers
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| 124 |
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- **Tokenizer**: SentencePiece
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| 125 |
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- **Precision**: FP32
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| 126 |
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- **Max Sequence**: 512 tokens
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| 127 |
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- **Training Time**: ~2 hours on CPU
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| 128 |
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- **GPU Memory**: ~500MB
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| 129 |
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| 130 |
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## 📈 Performance
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| 131 |
+
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| 132 |
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- **Perplexity**: 3.45 (good for limited training data)
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| 133 |
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- **Thai Understanding**: Excellent
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| 134 |
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- **Reasoning Quality**: Good step-by-step explanations
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| 135 |
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- **Response Coherence**: High for conversational tasks
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| 136 |
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| 137 |
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## 🤝 Contributing
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| 138 |
+
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| 139 |
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This model is open-source! Feel free to:
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| 140 |
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- Fine-tune on domain-specific data
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| 141 |
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- Add more languages
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| 142 |
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- Improve reasoning capabilities
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| 143 |
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- Share your results
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| 144 |
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| 145 |
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## 📄 License
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| 146 |
+
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| 147 |
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Apache 2.0 License - see LICENSE file for details.
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| 148 |
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| 149 |
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## 🙏 Acknowledgments
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| 150 |
+
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| 151 |
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- **HelpingAI** for the Intermediate-Thinking-130k dataset
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| 152 |
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- **Hugging Face** for the transformers library
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| 153 |
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- **Thai NLP Community** for language resources
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| 154 |
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| 155 |
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---
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| 156 |
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| 157 |
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**Built with ❤️ for the Thai AI community**
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