# 🇹🇭 Chinda Opensource Thai LLM 4B **Latest Model, Think in Thai, Answer in Thai, Built by Thai Startup** ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/RTzTckBAT3MjYp950UamV.jpeg) Chinda Opensource Thai LLM 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](https://chindax.iapp.co.th) (Choose ChindaLLM 4b) - **📦 Model Download:** [https://huggingface.co/iapp/chinda-qwen3-4b](https://huggingface.co/iapp/chinda-qwen3-4b) - **🐋 Ollama:** [https://ollama.com/iapp/chinda-qwen3-4b](https://ollama.com/iapp/chinda-qwen3-4b) - **🏠 Homepage:** [https://iapp.co.th/products/chinda-opensource-llm](https://iapp.co.th/products/chinda-opensource-llm) - **📄 License:** Apache 2.0 ## ✨ Key Features ### 🆓 **0. Free and Opensource for Everyone** Chinda LLM 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 Chinda LLM 4B demonstrates superior performance compared to other Thai language models in its category: | Benchmark | Language | Chinda LLM 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 Chinda LLM 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 ```bash pip install transformers torch ``` ### Basic Usage ```python 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 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) ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Enable detailed reasoning ) ``` #### Disable Thinking Mode (For Efficiency) ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Fast response mode ) ``` ### API Deployment #### Using vLLM ```bash pip install vllm>=0.8.5 vllm serve iapp/chinda-qwen3-4b --enable-reasoning --reasoning-parser deepseek_r1 ``` #### Using SGLang ```bash 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:** ```bash # Install Ollama (if not already installed) curl -fsSL https://ollama.com/install.sh | sh # Pull Chinda LLM 4B model ollama pull iapp/chinda-qwen3-4b ``` **Basic Usage:** ```bash # Start chatting with Chinda LLM ollama run iapp/chinda-qwen3-4b # Example conversation ollama run iapp/chinda-qwen3-4b "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย" ``` **API Server:** ```bash # 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:** - **Size:** 2.5GB (quantized) - **Context Window:** 40K tokens - **Architecture:** Optimized for local deployment - **Performance:** Fast inference on consumer hardware ## 🔧 Advanced Configuration ### Processing Long Texts Chinda LLM 4B natively supports up to 32,768 tokens. For longer contexts, enable YaRN scaling: ```json { "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 Chinda LLM 4B uses a standardized chat template format for consistent interactions: ```python # 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 ```python # 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: ```python # 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 ```python 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](https://iapp.co.th) ## ❓ Frequently Asked Questions

🏷️ Why is it named "Chinda"?

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, Chinda LLM 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.

⚖️ Can I use Chinda LLM 4B for commercial purposes?

Yes! Chinda LLM 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!

🧠 What's the difference between thinking and non-thinking mode?

**Thinking Mode (`enable_thinking=True`):** - Model shows its reasoning process in `...` 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.

📊 How does Chinda LLM 4B compare to other Thai language models?

Chinda LLM 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**.

💻 What are the system requirements to run Chinda LLM 4B?

**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)

🔧 Can I fine-tune Chinda LLM 4B for my specific use case?

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

🌍 What languages does Chinda LLM 4B support?

**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

🔍 Is the training data publicly available?

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.

🆘 How do I get support or report issues?

**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

📥 How large is the model download and what format is it in?

**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

## 📚 Citation If you use Chinda LLM 4B in your research or projects, please cite: ```bibtex @misc{chinda-llm-4b, title={Chinda LLM 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* ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/qNa4bznh179myghTFcpFp.jpeg) **Powered by iApp Technology** Disclaimer: Provided responses are not guaranteed.