Instructions to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Ollama
How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Ollama:
```
ollama run hf.co/iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
```

Unsloth Studio

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF to start chatting

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Docker Model Runner:
```
docker model run hf.co/iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M
```

Lemonade

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenThai 2.0 Legal 30B-A3B — GGUF

Official GGUF quantizations of iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b

Website · Announcement · Live demo · Discord

An open-weight Thai legal LLM that recalls Thai statutes and cites the exact law name and section (มาตรา) as structured JSON. 30B Mixture-of-Experts with only ~3B parameters active per token — which is why a 30B model runs comfortably on modest hardware.

Use it with retrieval. Open-book citation accuracy is 0.99 versus 0.07–0.40 from pure memory. Pair it with OpenThaiRAG or your own retrieval over authoritative statute text.

Quants

File	Quant	Size	Notes
`openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b.Q4_K_M.gguf`	Q4_K_M	~18 GB	Recommended. Fits a 24 GB GPU.
`openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b.Q5_K_M.gguf`	Q5_K_M	~21 GB	Higher quality.
`openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b.Q8_0.gguf`	Q8_0	~32 GB	Near-lossless.

Usage

Ollama

ollama run hf.co/iapp/openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b-GGUF:Q4_K_M

llama.cpp

llama-cli -m openthai2.0-legal-thaillm-nemotron-3-nano-30b-a3b.Q4_K_M.gguf \
  -p "ลักทรัพย์ในเวลากลางคืน ผิดมาตราใด" -n 1024 --temp 0.3

The chat template is embedded in the GGUF. Recommended sampling: temperature=0.3, top_p=0.9. Architecture is a hybrid Mamba2-Transformer MoE (NVIDIA Nemotron-3-Nano-30B-A3B base) — use a recent llama.cpp build.

⚠️ Responsible use

Outputs are decision support, not legal advice. Verify every citation against the current statute text. Near-miss rejection — telling the governing section from a closely related one — is the hardest task for every model tested, this one included.

Citation

@misc{openthai2026legal,
  title  = {OpenThai 2.0 Legal: An Open-Weight Thai Legal Language Model},
  author = {Viriyayudhakorn, Kobkrit and Yuenyong, Sumeth and Chay-intr, Thodsaporn},
  year   = {2026},
  url    = {https://openthai.aieat.or.th/openthai2p0-legal}
}

OpenThai (formerly OpenThaiGPT) — free, open-weight Thai large language models from AIEAT and iApp Technology, built here on the NVIDIA Nemotron and NeMo stack. With thanks to the community members who published unofficial GGUF conversions before these existed.