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ChindaMT-4B-GGUF
bombman/ChindaMT-4B-GGUF
ChindaMT-4B-GGUF is a machine learning model from bombman. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
GGUF conversion of iapp/ChindaMT-4B for:
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.gguf20.8 GB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- May 16, 2026
- Updated
- May 16, 2026
- SHA
- ffbe2694
Base models
Model card
From the Hugging Face model README
🇹🇭 ChindaMT-4B GGUF
GGUF conversion of iapp/ChindaMT-4B for:
- llama.cpp
- Ollama
- LM Studio
- Open WebUI
- Local AI workflows
ChindaMT-4B is a Thai-focused LLM developed by iApp Technology, optimized for Thai language understanding, reasoning, and local AI deployment.
📦 Available Quantizations
| Quant | Quality | Speed | RAM Usage | Recommended For |
|---|---|---|---|---|
| F16 | Best | Slow | High | Benchmark / Fine-tune |
| Q8_0 | Excellent | Medium | Medium-High | High-end GPU |
| Q4_K_M | Very Good | Fast | Medium | Daily usage ⭐ |
| Q4_K_S | Good | Very Fast | Low | Laptop / Edge device |
⭐ Recommended Version
Q4_K_M
Best balance between:
- Quality
- Speed
- Memory usage
📥 Download
Available files:
ChindaMT-4B-F16.gguf
ChindaMT-4B-Q8_0.gguf
ChindaMT-4B-Q4_K_M.gguf
ChindaMT-4B-Q4_K_S.gguf
🚀 Usage
llama.cpp
CLI
./llama-cli \
-m ChindaMT-4B-Q4_K_M.gguf \
-p "สวัสดี ช่วยอธิบาย quantum computing แบบง่าย"
Server
./llama-server \
-m ChindaMT-4B-Q4_K_M.gguf \
--host 0.0.0.0 \
--port 8080
🦙 Ollama
Create Modelfile
FROM ./ChindaMT-4B-Q4_K_M.gguf
Build
ollama create chindamt -f Modelfile
Run
ollama run chindamt
💻 LM Studio
- Open LM Studio
- Go to Models
- Import
.gguf - Select quantization
- Start chatting
Recommended:
Q4_K_Mfor most usersQ8_0if you have more VRAM
⚡ Hardware Recommendation
| Quant | Recommended RAM |
|---|---|
| Q4_K_S | 6GB+ |
| Q4_K_M | 8GB+ |
| Q8_0 | 12GB+ |
| F16 | 16GB+ |
GPU acceleration supported:
- CUDA
- Vulkan
- Metal
- ROCm
🧠 Quantization Notes
F16
Highest quality and accuracy.
Best for:
- Benchmarking
- Fine-tuning
- Powerful GPUs
Q8_0
Very close to F16 quality with reduced memory usage.
Best for:
- Workstations
- Desktop GPUs
Q4_K_M ⭐
Best overall balance.
Most recommended quantization for:
- Daily usage
- AI agents
- Local assistants
Q4_K_S
Smallest and fastest version.
Best for:
- Laptops
- Raspberry Pi
- Low RAM systems
🔥 Suggested Use Cases
- Thai AI Assistant
- Coding Assistant
- Local RAG
- Obsidian AI
- Open WebUI
- n8n AI Workflow
- Autonomous Agent
- Raspberry Pi AI
📊 Base Model
- Base: Qwen3-4B
- Optimized for Thai language
- Developed by iApp Technology
- License: Apache 2.0
Official links:
- https://huggingface.co/iapp/ChindaMT-4B
- https://huggingface.co/iapp/chinda-qwen3-4b-gguf
- https://iapp.co.th/openmodels/chinda-opensource-llm
📜 License
Apache 2.0
Commercial use allowed.
🙌 Credits
- iApp Technology
- Qwen Team
- llama.cpp
- GGUF community
❤️ Thai Open Source AI
Built for Thai developers 🇹🇭
- License
- apache-2.0
- Languages
- th, en
- Created
- May 16, 2026
- Updated
- May 16, 2026