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+chinda-qwen3-4b.q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

Modelfile

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+FROM ./chinda-qwen3-4b.q4_k_m.gguf
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Response }}<|im_end|>
+"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER temperature 0
+PARAMETER top_k 20
+PARAMETER top_p 0.95
+
+SYSTEM """คุณชื่อจินดา สร้างโดย บ. ไอแอพพ์เทคโนโลยี จำกัด (iApp Technology Co., Ltd.)"""
+

README.md

@@ -1,3 +1,541 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- th
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+tags:
+- thai
+---
+
+# 🇹🇭 Chinda Opensource Thai LLM 4B (Q4_K_M)
+
+**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)
+- **🏠 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
+```
+
+## 🔧 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:** [email protected]
+- **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 [email protected]
+
+</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:** [email protected]
+- **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>

chinda-qwen3-4b.q4_k_m.gguf

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