---

license: apache-2.0

language:

• th
• en

base_model: iapp/chinda-qwen3-4b

pipeline_tag: text-generation

tags:

• thai

---

🇹🇭 OpenThai Chinda 4B (GGUF Q4_K_M)

Latest Model, Think in Thai, Answer in Thai, Built by Thai Startup

!openthai-chinda-4b-v1-benchmark

OpenThai Chinda 4B is iApp Technology's cutting-edge Thai language model that brings advanced thinking capabilities to the Thai AI ecosystem. Built on the latest Qwen3-4B architecture, Chinda represents our commitment to developing sovereign AI solutions for Thailand.

🚀 Quick Links

• 🌐 Demo: https://chindax.iapp.co.th (Choose OpenThai Chinda 4B)
• 📦 Model Download: https://huggingface.co/iapp/chinda-qwen3-4b
• 🐋 Ollama: https://ollama.com/iapp/chinda-qwen3-4b
• 🏠 Homepage: https://iapp.co.th/openmodels/openthai-chinda-opensource-llm
• 📄 License: Apache 2.0

✨ Key Features

🆓 0. Free and Opensource for Everyone

OpenThai Chinda 4B is completely free and open-source, enabling developers, researchers, and businesses to build Thai AI applications without restrictions.

🧠 1. Advanced Thinking Model

• Highest score among Thai LLMs in 4B category
• Seamless switching between thinking and non-thinking modes
• Superior reasoning capabilities for complex problems
• Can be turned off for efficient general-purpose dialogue

🇹🇭 2. Exceptional Thai Language Accuracy

• 98.4% accuracy in outputting Thai language
• Prevents unwanted Chinese and foreign language outputs
• Specifically fine-tuned for Thai linguistic patterns

🆕 3. Latest Architecture

• Based on the cutting-edge Qwen3-4B model
• Incorporates the latest advancements in language modeling
• Optimized for both performance and efficiency

📜 4. Apache 2.0 License

• Commercial use permitted
• Modification and distribution allowed
• No restrictions on private use

📊 Benchmark Results

OpenThai Chinda 4B demonstrates superior performance compared to other Thai language models in its category:

| Benchmark | Language | OpenThai Chinda 4B | Alternative* |

|-----------|----------|---------------|-------------|

| AIME24 | English | 0.533 | 0.100 |

| | Thai | 0.100 | 0.000 |

| LiveCodeBench | English | 0.665 | 0.209 |

| | Thai | 0.198 | 0.144 |

| MATH500 | English | 0.908 | 0.702 |

| | Thai | 0.612 | 0.566 |

| IFEVAL | English | 0.849 | 0.848 |

| | Thai | 0.683 | 0.740 |

| Language Accuracy | Thai | 0.984 | 0.992 |

| OpenThaiEval | Thai | 0.651 | 0.544 |

| AVERAGE | | 0.569 | 0.414 |

• Alternative: scb10x_typhoon2.1-gemma3-4b
• Tested by Skythought and Evalscope Benchmark Libraries by iApp Technology team. Results show OpenThai Chinda 4B achieving 37% better overall performance than the nearest alternative.

✅ Suitable For

🔍 1. RAG Applications (Sovereign AI)

Perfect for building Retrieval-Augmented Generation systems that keep data processing within Thai sovereignty.

📱 2. Mobile and Laptop Applications

Reliable Small Language Model optimized for edge computing and personal devices.

🧮 3. Math Calculation

Excellent performance in mathematical reasoning and problem-solving.

💻 4. Code Assistant

Strong capabilities in code generation and programming assistance.

⚡ 5. Resource Efficiency

Very fast inference with minimal GPU memory consumption, ideal for production deployments.

❌ Not Suitable For

📚 Factual Questions Without Context

As a 4B parameter model, it may hallucinate when asked for specific facts without provided context. Always use with RAG or provide relevant context for factual queries.

🛠️ Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "iapp/chinda-qwen3-4b"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Prepare the model input
prompt = "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย"
messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Enable thinking mode for better reasoning
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024,
    temperature=0.6,
    top_p=0.95,
    top_k=20,
    do_sample=True
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content (if enabled)
try:
    # Find </think> token (151668)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("🧠 Thinking:", thinking_content)
print("💬 Response:", content)

Switching Between Thinking and Non-Thinking Mode

Enable Thinking Mode (Default)

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Enable detailed reasoning
)

Disable Thinking Mode (For Efficiency)

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False  # Fast response mode
)

API Deployment

Using vLLM

pip install vllm>=0.8.5
vllm serve iapp/chinda-qwen3-4b --enable-reasoning --reasoning-parser deepseek_r1

Using SGLang

pip install sglang>=0.4.6.post1
python -m sglang.launch_server --model-path iapp/chinda-qwen3-4b --reasoning-parser qwen3

Using Ollama (Easy Local Setup)

Installation:

# Install Ollama (if not already installed)
curl -fsSL https://ollama.com/install.sh | sh

# Pull OpenThai Chinda 4B model
ollama pull iapp/chinda-qwen3-4b

Basic Usage:

# Start chatting with OpenThai Chinda
ollama run iapp/chinda-qwen3-4b

# Example conversation
ollama run iapp/chinda-qwen3-4b "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย"

API Server:

# Start Ollama API server
ollama serve

# Use with curl
curl http://localhost:11434/api/generate -d '{
  "model": "iapp/chinda-qwen3-4b",
  "prompt": "สวัสดีครับ",
  "stream": false
}'

Model Specifications:<br>

• Size: 2.5GB (quantized)<br>
• Context Window: 40K tokens<br>
• Architecture: Optimized for local deployment<br>
• Performance: Fast inference on consumer hardware<br>

🔧 Advanced Configuration

Processing Long Texts

OpenThai Chinda 4B natively supports up to 32,768 tokens. For longer contexts, enable YaRN scaling:

{
    "rope_scaling": {
        "rope_type": "yarn",
        "factor": 4.0,
        "original_max_position_embeddings": 32768
    }
}

Recommended Parameters

For Thinking Mode:

• Temperature: 0.6
• Top-P: 0.95
• Top-K: 20
• Min-P: 0

For Non-Thinking Mode:

• Temperature: 0.7
• Top-P: 0.8
• Top-K: 20
• Min-P: 0

📝 Context Length & Template Format

Context Length Support

• Native Context Length: 32,768 tokens
• Extended Context Length: Up to 131,072 tokens (with YaRN scaling)
• Input + Output: Total conversation length supported
• Recommended Usage: Keep conversations under 32K tokens for optimal performance

Chat Template Format

OpenThai Chinda 4B uses a standardized chat template format for consistent interactions:

# Basic template structure
messages = [
    {"role": "system", "content": "You are a helpful Thai AI assistant."},
    {"role": "user", "content": "สวัสดีครับ"},
    {"role": "assistant", "content": "สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ"},
    {"role": "user", "content": "ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย"}
]

# Apply template with thinking mode
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

Template Structure

The template follows the standard conversational format:

<|im_start|>system
You are a helpful Thai AI assistant.<|im_end|>
<|im_start|>user
สวัสดีครับ<|im_end|>
<|im_start|>assistant
สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ<|im_end|>
<|im_start|>user
ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย<|im_end|>
<|im_start|>assistant

Advanced Template Usage

# Multi-turn conversation with thinking control
def create_conversation(messages, enable_thinking=True):
    # Add system message if not present
    if not messages or messages[0]["role"] != "system":
        system_msg = {
            "role": "system", 
            "content": "คุณเป็น AI ผู้ช่วยที่ฉลาดและเป็นประโยชน์ พูดภาษาไทยได้อย่างเป็นธรรมชาติ"
        }
        messages = [system_msg] + messages
    
    # Apply chat template
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=enable_thinking
    )
    
    return text

# Example usage
conversation = [
    {"role": "user", "content": "คำนวณ 15 × 23 = ?"},
]

prompt = create_conversation(conversation, enable_thinking=True)

Dynamic Mode Switching

You can control thinking mode within conversations using special commands:

# Enable thinking for complex problems
messages = [
    {"role": "user", "content": "/think แก้สมการ: x² + 5x - 14 = 0"}
]

# Disable thinking for quick responses  
messages = [
    {"role": "user", "content": "/no_think สวัสดี"}
]

Context Management Best Practices

1. Monitor Token Count: Keep track of total tokens (input + output)
2. Truncate Old Messages: Remove oldest messages when approaching limits
3. Use YaRN for Long Contexts: Enable rope scaling for documents > 32K tokens
4. Batch Processing: For very long texts, consider chunking and processing in batches

def manage_context(messages, max_tokens=30000):
    """Simple context management function"""
    total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
    
    while total_tokens > max_tokens and len(messages) > 2:
        # Keep system message and remove oldest user/assistant pair
        if messages[1]["role"] == "user":
            messages.pop(1)  # Remove user message
            if len(messages) > 1 and messages[1]["role"] == "assistant":
                messages.pop(1)  # Remove corresponding assistant message
        
        total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
    
    return messages

🏢 Enterprise Support

For enterprise deployments, custom training, or commercial support, contact us at:

• Email: sale@iapp.co.th
• Website: iapp.co.th

❓ Frequently Asked Questions

<details>

<summary><strong>🏷️ Why is it named "Chinda"?</strong></summary>

The name "Chinda" (จินดา) comes from "จินดามณี" (Chindamani), which is considered the first book of Thailand written by Phra Horathibodi (Sri Dharmasokaraja) in the Sukhothai period. Just as จินดามณี was a foundational text for Thai literature and learning, OpenThai Chinda represents our foundation for Thai sovereign AI - a model that truly understands and thinks in Thai, preserving and advancing Thai language capabilities in the digital age.

</details>

<details>

<summary><strong>⚖️ Can I use OpenThai Chinda 4B for commercial purposes?</strong></summary>

Yes! OpenThai Chinda 4B is released under the Apache 2.0 License, which allows:

• ✅ Commercial use - Use in commercial products and services
• ✅ Research use - Academic and research applications
• ✅ Modification - Adapt and modify the model
• ✅ Distribution - Share and redistribute the model
• ✅ Private use - Use for internal company projects

No restrictions on commercial applications - build and deploy freely!

</details>

<details>

<summary><strong>🧠 What's the difference between thinking and non-thinking mode?</strong></summary>

Thinking Mode (enable_thinking=True):

• Model shows its reasoning process in <think>...</think> blocks
• Better for complex problems, math, coding, logical reasoning
• Slower but more accurate responses
• Recommended for tasks requiring deep analysis

Non-Thinking Mode (enable_thinking=False):

• Direct answers without showing reasoning
• Faster responses for general conversations
• Better for simple queries and chat applications
• More efficient resource usage

You can switch between modes or let users control it dynamically using /think and /no_think commands.

</details>

<details>

<summary><strong>📊 How does OpenThai Chinda 4B compare to other Thai language models?</strong></summary>

OpenThai Chinda 4B achieves 37% better overall performance compared to the nearest alternative:

• Overall Average: 0.569 vs 0.414 (alternative)
• Math (MATH500): 0.908 vs 0.702 (English), 0.612 vs 0.566 (Thai)
• Code (LiveCodeBench): 0.665 vs 0.209 (English), 0.198 vs 0.144 (Thai)
• Thai Language Accuracy: 98.4% (prevents Chinese/foreign text output)
• OpenThaiEval: 0.651 vs 0.544

It's currently the highest-scoring Thai LLM in the 4B parameter category.

</details>

<details>

<summary><strong>💻 What are the system requirements to run OpenThai Chinda 4B?</strong></summary>

Minimum Requirements:

• GPU: 8GB VRAM (RTX 3070/4060 Ti or better)
• RAM: 16GB system memory
• Storage: 8GB free space for model download
• Python: 3.8+ with PyTorch

Recommended for Production:

• GPU: 16GB+ VRAM (RTX 4080/A4000 or better)
• RAM: 32GB+ system memory
• Storage: SSD for faster loading

CPU-Only Mode: Possible but significantly slower (not recommended for production)

</details>

<details>

<summary><strong>🔧 Can I fine-tune OpenThai Chinda 4B for my specific use case?</strong></summary>

Yes! As an open-source model under Apache 2.0 license, you can:

• Fine-tune on your domain-specific data
• Customize for specific tasks or industries
• Modify the architecture if needed
• Create derivatives for specialized applications

Popular fine-tuning frameworks that work with Chinda:

• Unsloth - Fast and memory-efficient
• LoRA/QLoRA - Parameter-efficient fine-tuning
• Hugging Face Transformers - Full fine-tuning
• Axolotl - Advanced training configurations

Need help with fine-tuning? Contact our team at sale@iapp.co.th

</details>

<details>

<summary><strong>🌍 What languages does OpenThai Chinda 4B support?</strong></summary>

Primary Languages:

• Thai - Native-level understanding and generation (98.4% accuracy)
• English - Strong performance across all benchmarks

Additional Languages:

• 100+ languages supported (inherited from Qwen3-4B base)
• Focus optimized for Thai-English bilingual tasks
• Code generation in multiple programming languages

Special Features:

• Code-switching between Thai and English
• Translation between Thai and other languages
• Multilingual reasoning capabilities

</details>

<details>

<summary><strong>🔍 Is the training data publicly available?</strong></summary>

The model weights are open-source, but the specific training datasets are not publicly released. However:

• Base Model: Built on Qwen3-4B (Alibaba's open foundation)
• Thai Optimization: Custom dataset curation for Thai language tasks
• Quality Focus: Carefully selected high-quality Thai content
• Privacy Compliant: No personal or sensitive data included

For research collaborations or dataset inquiries, contact our research team.

</details>

<details>

<summary><strong>🆘 How do I get support or report issues?</strong></summary>

For Technical Issues:

• GitHub Issues: Report bugs and technical problems
• Hugging Face: Model-specific questions and discussions
• Documentation: Check our comprehensive guides

For Commercial Support:

• Email: sale@iapp.co.th
• Enterprise Support: Custom training, deployment assistance
• Consulting: Integration and optimization services

Community Support:

• Thai AI Community: Join discussions about Thai AI development
• Developer Forums: Connect with other Chinda users

</details>

<details>

<summary><strong>📥 How large is the model download and what format is it in?</strong></summary>

Model Specifications:

• Parameters: 4.02 billion (4B)
• Download Size: ~8GB (compressed)
• Format: Safetensors (recommended) and PyTorch
• Precision: BF16 (Brain Float 16)

Download Options:

• Hugging Face Hub: huggingface.co/iapp/chinda-qwen3-4b
• Git LFS: For version control integration
• Direct Download: Individual model files
• Quantized Versions: Available for reduced memory usage (GGUF, AWQ)

Quantization Options:

• 4-bit (GGUF): ~2.5GB, runs on 4GB VRAM
• 8-bit: ~4GB, balanced performance/memory
• 16-bit (Original): ~8GB, full performance

</details>

📚 Citation

If you use OpenThai Chinda 4B in your research or projects, please cite:

@misc{chinda-llm-4b,
  title={OpenThai Chinda 4B: Thai Sovereign AI Language Model},
  author={iApp Technology},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/iapp/chinda-qwen3-4b}
}

---

Built with 🇹🇭 by iApp Technology - Empowering Thai Businesses with Sovereign AI Excellence

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<i>Disclaimer: Provided responses are not guaranteed.</i>