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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: iapp/chinda-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 (GGUF Q4_K_M) |
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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:**<br> |
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- **Size:** 2.5GB (quantized)<br> |
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- **Context Window:** 40K tokens<br> |
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- **Architecture:** Optimized for local deployment<br> |
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- **Performance:** Fast inference on consumer hardware<br> |
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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:** [email protected] |
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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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</details> |
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<details> |
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<summary><strong>⚖️ Can I use Chinda LLM 4B for commercial purposes?</strong></summary> |
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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 |
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- ✅ **Research use** - Academic and research applications |
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- ✅ **Modification** - Adapt and modify the model |
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- ✅ **Distribution** - Share and redistribute the model |
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- ✅ **Private use** - Use for internal company projects |
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No restrictions on commercial applications - build and deploy freely! |
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</details> |
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<details> |
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<summary><strong>🧠 What's the difference between thinking and non-thinking mode?</strong></summary> |
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**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 |
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- Recommended for tasks requiring deep analysis |
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**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 |
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- More efficient resource usage |
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You can switch between modes or let users control it dynamically using `/think` and `/no_think` commands. |
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</details> |
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<details> |
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<summary><strong>📊 How does Chinda LLM 4B compare to other Thai language models?</strong></summary> |
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Chinda LLM 4B achieves **37% better overall performance** compared to the nearest alternative: |
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- **Overall Average:** 0.569 vs 0.414 (alternative) |
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- **Math (MATH500):** 0.908 vs 0.702 (English), 0.612 vs 0.566 (Thai) |
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- **Code (LiveCodeBench):** 0.665 vs 0.209 (English), 0.198 vs 0.144 (Thai) |
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- **Thai Language Accuracy:** 98.4% (prevents Chinese/foreign text output) |
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- **OpenThaiEval:** 0.651 vs 0.544 |
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It's currently the **highest-scoring Thai LLM in the 4B parameter category**. |
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</details> |
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<details> |
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<summary><strong>💻 What are the system requirements to run Chinda LLM 4B?</strong></summary> |
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**Minimum Requirements:** |
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- **GPU:** 8GB VRAM (RTX 3070/4060 Ti or better) |
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- **RAM:** 16GB system memory |
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- **Storage:** 8GB free space for model download |
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- **Python:** 3.8+ with PyTorch |
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**Recommended for Production:** |
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- **GPU:** 16GB+ VRAM (RTX 4080/A4000 or better) |
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- **RAM:** 32GB+ system memory |
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- **Storage:** SSD for faster loading |
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**CPU-Only Mode:** Possible but significantly slower (not recommended for production) |
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</details> |
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<details> |
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<summary><strong>🔧 Can I fine-tune Chinda LLM 4B for my specific use case?</strong></summary> |
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Yes! As an open-source model under Apache 2.0 license, you can: |
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- **Fine-tune** on your domain-specific data |
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- **Customize** for specific tasks or industries |
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- **Modify** the architecture if needed |
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- **Create derivatives** for specialized applications |
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Popular fine-tuning frameworks that work with Chinda: |
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- **Unsloth** - Fast and memory-efficient |
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- **LoRA/QLoRA** - Parameter-efficient fine-tuning |
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- **Hugging Face Transformers** - Full fine-tuning |
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- **Axolotl** - Advanced training configurations |
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Need help with fine-tuning? Contact our team at [email protected] |
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</details> |
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<details> |
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<summary><strong>🌍 What languages does Chinda LLM 4B support?</strong></summary> |
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**Primary Languages:** |
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- **Thai** - Native-level understanding and generation (98.4% accuracy) |
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- **English** - Strong performance across all benchmarks |
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**Additional Languages:** |
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- 100+ languages supported (inherited from Qwen3-4B base) |
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- Focus optimized for Thai-English bilingual tasks |
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- Code generation in multiple programming languages |
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**Special Features:** |
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- **Code-switching** between Thai and English |
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- **Translation** between Thai and other languages |
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- **Multilingual reasoning** capabilities |
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</details> |
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<details> |
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<summary><strong>🔍 Is the training data publicly available?</strong></summary> |
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The model weights are open-source, but the specific training datasets are not publicly released. However: |
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- **Base Model:** Built on Qwen3-4B (Alibaba's open foundation) |
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- **Thai Optimization:** Custom dataset curation for Thai language tasks |
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- **Quality Focus:** Carefully selected high-quality Thai content |
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- **Privacy Compliant:** No personal or sensitive data included |
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For research collaborations or dataset inquiries, contact our research team. |
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</details> |
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<details> |
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<summary><strong>🆘 How do I get support or report issues?</strong></summary> |
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**For Technical Issues:** |
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- **GitHub Issues:** Report bugs and technical problems |
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- **Hugging Face:** Model-specific questions and discussions |
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- **Documentation:** Check our comprehensive guides |
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**For Commercial Support:** |
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- **Email:** [email protected] |
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- **Enterprise Support:** Custom training, deployment assistance |
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- **Consulting:** Integration and optimization services |
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**Community Support:** |
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- **Thai AI Community:** Join discussions about Thai AI development |
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- **Developer Forums:** Connect with other Chinda users |
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</details> |
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<details> |
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<summary><strong>📥 How large is the model download and what format is it in?</strong></summary> |
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**Model Specifications:** |
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- **Parameters:** 4.02 billion (4B) |
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- **Download Size:** ~8GB (compressed) |
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- **Format:** Safetensors (recommended) and PyTorch |
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- **Precision:** BF16 (Brain Float 16) |
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**Download Options:** |
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- **Hugging Face Hub:** `huggingface.co/iapp/chinda-qwen3-4b` |
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- **Git LFS:** For version control integration |
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- **Direct Download:** Individual model files |
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- **Quantized Versions:** Available for reduced memory usage (GGUF, AWQ) |
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**Quantization Options:** |
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- **4-bit (GGUF):** ~2.5GB, runs on 4GB VRAM |
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- **8-bit:** ~4GB, balanced performance/memory |
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- **16-bit (Original):** ~8GB, full performance |
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</details> |
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## 📚 Citation |
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If you use Chinda LLM 4B in your research or projects, please cite: |
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```bibtex |
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@misc{chinda-llm-4b, |
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title={Chinda LLM 4B: Thai Sovereign AI Language Model}, |
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author={iApp Technology}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/iapp/chinda-qwen3-4b} |
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} |
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``` |
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--- |
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*Built with 🇹🇭 by iApp Technology - Empowering Thai Businesses with Sovereign AI Excellence* |
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**Powered by iApp Technology** |
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<i>Disclaimer: Provided responses are not guaranteed.</i> |
