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---
license: apache-2.0
language:
- th
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- thai
---
# 🇹🇭 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 </think> token (151668)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("🧠 Thinking:", thinking_content)
print("💬 Response:", content)
```
### Switching Between Thinking and Non-Thinking Mode
#### Enable Thinking Mode (Default)
```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
<details>
<summary><strong>🏷️ Why is it named "Chinda"?</strong></summary>
The name "Chinda" (จินดา) comes from "จินดามณี" (Chindamani), which is considered the first book of Thailand written by Phra Horathibodi (Sri Dharmasokaraja) in the Sukhothai period. Just as จินดามณี was a foundational text for Thai literature and learning, 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.
</details>
<details>
<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:
- ✅ **Commercial use** - Use in commercial products and services
- ✅ **Research use** - Academic and research applications
- ✅ **Modification** - Adapt and modify the model
- ✅ **Distribution** - Share and redistribute the model
- ✅ **Private use** - Use for internal company projects
No restrictions on commercial applications - build and deploy freely!
</details>
<details>
<summary><strong>🧠 What's the difference between thinking and non-thinking mode?</strong></summary>
**Thinking Mode (`enable_thinking=True`):**
- Model shows its reasoning process in `<think>...</think>` blocks
- Better for complex problems, math, coding, logical reasoning
- Slower but more accurate responses
- Recommended for tasks requiring deep analysis
**Non-Thinking Mode (`enable_thinking=False`):**
- Direct answers without showing reasoning
- Faster responses for general conversations
- Better for simple queries and chat applications
- More efficient resource usage
You can switch between modes or let users control it dynamically using `/think` and `/no_think` commands.
</details>
<details>
<summary><strong>📊 How does 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*
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/qNa4bznh179myghTFcpFp.jpeg)
**Powered by iApp Technology**
<i>Disclaimer: Provided responses are not guaranteed.</i>

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{
"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
"<tool_call>": 151657,
"<tool_response>": 151665,
"<|box_end|>": 151649,
"<|box_start|>": 151648,
"<|endoftext|>": 151643,
"<|file_sep|>": 151664,
"<|fim_middle|>": 151660,
"<|fim_pad|>": 151662,
"<|fim_prefix|>": 151659,
"<|fim_suffix|>": 151661,
"<|im_end|>": 151645,
"<|im_start|>": 151644,
"<|image_pad|>": 151655,
"<|object_ref_end|>": 151647,
"<|object_ref_start|>": 151646,
"<|quad_end|>": 151651,
"<|quad_start|>": 151650,
"<|repo_name|>": 151663,
"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
"<|vision_pad|>": 151654,
"<|vision_start|>": 151652
}

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
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"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
"content": "</think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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