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Model: iapp/chinda-qwen3-4b Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- th
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- en
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base_model:
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- Qwen/Qwen3-4B
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pipeline_tag: text-generation
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tags:
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- thai
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---
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# 🇹🇭 Chinda Opensource Thai LLM 4B
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**Latest Model, Think in Thai, Answer in Thai, Built by Thai Startup**
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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.
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## 🚀 Quick Links
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- **🌐 Demo:** [https://chindax.iapp.co.th](https://chindax.iapp.co.th) (Choose ChindaLLM 4b)
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- **📦 Model Download:** [https://huggingface.co/iapp/chinda-qwen3-4b](https://huggingface.co/iapp/chinda-qwen3-4b)
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- **🐋 Ollama:** [https://ollama.com/iapp/chinda-qwen3-4b](https://ollama.com/iapp/chinda-qwen3-4b)
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- **🏠 Homepage:** [https://iapp.co.th/products/chinda-opensource-llm](https://iapp.co.th/products/chinda-opensource-llm)
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- **📄 License:** Apache 2.0
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## ✨ Key Features
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### 🆓 **0. Free and Opensource for Everyone**
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Chinda LLM 4B is completely free and open-source, enabling developers, researchers, and businesses to build Thai AI applications without restrictions.
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### 🧠 **1. Advanced Thinking Model**
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- **Highest score among Thai LLMs in 4B category**
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- Seamless switching between thinking and non-thinking modes
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- Superior reasoning capabilities for complex problems
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- Can be turned off for efficient general-purpose dialogue
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### 🇹🇭 **2. Exceptional Thai Language Accuracy**
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- **98.4% accuracy** in outputting Thai language
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- Prevents unwanted Chinese and foreign language outputs
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- Specifically fine-tuned for Thai linguistic patterns
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### 🆕 **3. Latest Architecture**
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- Based on the cutting-edge **Qwen3-4B** model
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- Incorporates the latest advancements in language modeling
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- Optimized for both performance and efficiency
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### 📜 **4. Apache 2.0 License**
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- Commercial use permitted
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- Modification and distribution allowed
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- No restrictions on private use
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## 📊 Benchmark Results
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Chinda LLM 4B demonstrates superior performance compared to other Thai language models in its category:
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| Benchmark | Language | Chinda LLM 4B | Alternative* |
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|-----------|----------|---------------|-------------|
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| **AIME24** | English | **0.533** | 0.100 |
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| | Thai | **0.100** | 0.000 |
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| **LiveCodeBench** | English | **0.665** | 0.209 |
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| | Thai | **0.198** | 0.144 |
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| **MATH500** | English | **0.908** | 0.702 |
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| | Thai | **0.612** | 0.566 |
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| **IFEVAL** | English | **0.849** | 0.848 |
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| | Thai | 0.683 | **0.740** |
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| **Language Accuracy** | Thai | 0.984 | **0.992** |
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| **OpenThaiEval** | Thai | **0.651** | 0.544 |
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| **AVERAGE** | | **0.569** | 0.414 |
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* Alternative: scb10x_typhoon2.1-gemma3-4b
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* 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.
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## ✅ Suitable For
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### 🔍 **1. RAG Applications (Sovereign AI)**
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Perfect for building Retrieval-Augmented Generation systems that keep data processing within Thai sovereignty.
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### 📱 **2. Mobile and Laptop Applications**
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Reliable Small Language Model optimized for edge computing and personal devices.
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### 🧮 **3. Math Calculation**
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Excellent performance in mathematical reasoning and problem-solving.
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### 💻 **4. Code Assistant**
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Strong capabilities in code generation and programming assistance.
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### ⚡ **5. Resource Efficiency**
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Very fast inference with minimal GPU memory consumption, ideal for production deployments.
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## ❌ Not Suitable For
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### 📚 **Factual Questions Without Context**
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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.
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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 AutoModelForCausalLM, AutoTokenizer
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model_name = "iapp/chinda-qwen3-4b"
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# Load the tokenizer and model
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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="auto",
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device_map="auto"
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)
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# Prepare the model input
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prompt = "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True # Enable thinking mode for better reasoning
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# Generate response
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=1024,
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temperature=0.6,
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top_p=0.95,
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top_k=20,
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do_sample=True
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# Parse thinking content (if enabled)
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try:
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# Find </think> token (151668)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("🧠 Thinking:", thinking_content)
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print("💬 Response:", content)
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```
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### Switching Between Thinking and Non-Thinking Mode
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#### Enable Thinking Mode (Default)
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```python
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True # Enable detailed reasoning
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)
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```
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#### Disable Thinking Mode (For Efficiency)
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```python
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False # Fast response mode
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)
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```
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### API Deployment
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#### Using vLLM
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```bash
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pip install vllm>=0.8.5
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vllm serve iapp/chinda-qwen3-4b --enable-reasoning --reasoning-parser deepseek_r1
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```
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#### Using SGLang
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```bash
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pip install sglang>=0.4.6.post1
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python -m sglang.launch_server --model-path iapp/chinda-qwen3-4b --reasoning-parser qwen3
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```
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#### Using Ollama (Easy Local Setup)
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**Installation:**
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```bash
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# Install Ollama (if not already installed)
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curl -fsSL https://ollama.com/install.sh | sh
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# Pull Chinda LLM 4B model
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ollama pull iapp/chinda-qwen3-4b
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```
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**Basic Usage:**
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```bash
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# Start chatting with Chinda LLM
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ollama run iapp/chinda-qwen3-4b
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# Example conversation
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ollama run iapp/chinda-qwen3-4b "อธิบายเกี่ยวกับปัญญาประดิษฐ์ให้ฟังหน่อย"
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```
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**API Server:**
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```bash
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# Start Ollama API server
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ollama serve
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# Use with curl
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curl http://localhost:11434/api/generate -d '{
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"model": "iapp/chinda-qwen3-4b",
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"prompt": "สวัสดีครับ",
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"stream": false
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}'
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```
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**Model Specifications:**
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- **Size:** 2.5GB (quantized)
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- **Context Window:** 40K tokens
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- **Architecture:** Optimized for local deployment
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- **Performance:** Fast inference on consumer hardware
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## 🔧 Advanced Configuration
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### Processing Long Texts
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Chinda LLM 4B natively supports up to 32,768 tokens. For longer contexts, enable YaRN scaling:
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```json
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{
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"rope_scaling": {
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"rope_type": "yarn",
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"factor": 4.0,
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"original_max_position_embeddings": 32768
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}
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}
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```
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### Recommended Parameters
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**For Thinking Mode:**
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- Temperature: 0.6
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- Top-P: 0.95
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- Top-K: 20
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- Min-P: 0
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**For Non-Thinking Mode:**
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- Temperature: 0.7
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- Top-P: 0.8
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- Top-K: 20
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- Min-P: 0
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## 📝 Context Length & Template Format
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### Context Length Support
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- **Native Context Length:** 32,768 tokens
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- **Extended Context Length:** Up to 131,072 tokens (with YaRN scaling)
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- **Input + Output:** Total conversation length supported
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- **Recommended Usage:** Keep conversations under 32K tokens for optimal performance
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### Chat Template Format
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Chinda LLM 4B uses a standardized chat template format for consistent interactions:
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```python
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# Basic template structure
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messages = [
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{"role": "system", "content": "You are a helpful Thai AI assistant."},
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{"role": "user", "content": "สวัสดีครับ"},
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{"role": "assistant", "content": "สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ"},
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{"role": "user", "content": "ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย"}
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]
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# Apply template with thinking mode
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True
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)
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```
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### Template Structure
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The template follows the standard conversational format:
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```
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<|im_start|>system
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You are a helpful Thai AI assistant.<|im_end|>
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<|im_start|>user
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สวัสดีครับ<|im_end|>
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<|im_start|>assistant
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สวัสดีค่ะ! มีอะไรให้ช่วยเหลือบ้างคะ<|im_end|>
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<|im_start|>user
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ช่วยอธิบายเรื่อง AI ให้ฟังหน่อย<|im_end|>
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<|im_start|>assistant
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```
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### Advanced Template Usage
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```python
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# Multi-turn conversation with thinking control
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def create_conversation(messages, enable_thinking=True):
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# Add system message if not present
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if not messages or messages[0]["role"] != "system":
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system_msg = {
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"role": "system",
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"content": "คุณเป็น AI ผู้ช่วยที่ฉลาดและเป็นประโยชน์ พูดภาษาไทยได้อย่างเป็นธรรมชาติ"
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}
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messages = [system_msg] + messages
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# Apply chat template
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=enable_thinking
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)
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return text
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# Example usage
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conversation = [
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{"role": "user", "content": "คำนวณ 15 × 23 = ?"},
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]
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prompt = create_conversation(conversation, enable_thinking=True)
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```
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### Dynamic Mode Switching
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You can control thinking mode within conversations using special commands:
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```python
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# Enable thinking for complex problems
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messages = [
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{"role": "user", "content": "/think แก้สมการ: x² + 5x - 14 = 0"}
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]
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# Disable thinking for quick responses
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messages = [
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{"role": "user", "content": "/no_think สวัสดี"}
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]
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```
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### Context Management Best Practices
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1. **Monitor Token Count:** Keep track of total tokens (input + output)
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2. **Truncate Old Messages:** Remove oldest messages when approaching limits
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3. **Use YaRN for Long Contexts:** Enable rope scaling for documents > 32K tokens
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4. **Batch Processing:** For very long texts, consider chunking and processing in batches
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```python
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def manage_context(messages, max_tokens=30000):
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"""Simple context management function"""
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total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
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while total_tokens > max_tokens and len(messages) > 2:
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# Keep system message and remove oldest user/assistant pair
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if messages[1]["role"] == "user":
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messages.pop(1) # Remove user message
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if len(messages) > 1 and messages[1]["role"] == "assistant":
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messages.pop(1) # Remove corresponding assistant message
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total_tokens = sum(len(tokenizer.encode(msg["content"])) for msg in messages)
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return messages
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```
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## 🏢 Enterprise Support
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For enterprise deployments, custom training, or commercial support, contact us at:
|
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- **Email:** sale@iapp.co.th
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- **Website:** [iapp.co.th](https://iapp.co.th)
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## ❓ Frequently Asked Questions
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<details>
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<summary><strong>🏷️ Why is it named "Chinda"?</strong></summary>
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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.
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|
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</details>
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|
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<details>
|
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<summary><strong>⚖️ Can I use Chinda LLM 4B for commercial purposes?</strong></summary>
|
||||
|
||||
Yes! Chinda LLM 4B is released under the **Apache 2.0 License**, which allows:
|
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- ✅ **Commercial use** - Use in commercial products and services
|
||||
- ✅ **Research use** - Academic and research applications
|
||||
- ✅ **Modification** - Adapt and modify the model
|
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- ✅ **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`):**
|
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- Model shows its reasoning process in `<think>...</think>` blocks
|
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- Better for complex problems, math, coding, logical reasoning
|
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- Slower but more accurate responses
|
||||
- Recommended for tasks requiring deep analysis
|
||||
|
||||
**Non-Thinking Mode (`enable_thinking=False`):**
|
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- Direct answers without showing reasoning
|
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- Faster responses for general conversations
|
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- 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 Chinda LLM 4B compare to other Thai language models?</strong></summary>
|
||||
|
||||
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**.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary><strong>💻 What are the system requirements to run Chinda LLM 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 Chinda LLM 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 Chinda LLM 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 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*
|
||||
|
||||
|
||||
|
||||
**Powered by iApp Technology**
|
||||
|
||||
<i>Disclaimer: Provided responses are not guaranteed.</i>
|
||||
Reference in New Issue
Block a user