Instructions to use openthaigpt/openthaigpt-1.6-72b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openthaigpt/openthaigpt-1.6-72b-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openthaigpt/openthaigpt-1.6-72b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openthaigpt/openthaigpt-1.6-72b-instruct")
model = AutoModelForCausalLM.from_pretrained("openthaigpt/openthaigpt-1.6-72b-instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openthaigpt/openthaigpt-1.6-72b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openthaigpt/openthaigpt-1.6-72b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openthaigpt/openthaigpt-1.6-72b-instruct

SGLang

How to use openthaigpt/openthaigpt-1.6-72b-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openthaigpt/openthaigpt-1.6-72b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openthaigpt/openthaigpt-1.6-72b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openthaigpt/openthaigpt-1.6-72b-instruct with Docker Model Runner:
```
docker model run hf.co/openthaigpt/openthaigpt-1.6-72b-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🇹🇭 OpenThaiGPT 1.6 72b

More Info

🇹🇭 OpenThaiGPT 1.6 72b is a 72-billion-parameter Thai language model designed for general purpose tasks with strong reasoning capabilities. Building upon the foundation of OpenThaiGPT 1.5, this latest version demonstrates improved performance across a variety of benchmarks, particularly in code reasoning and general language tasks.

📢 OpenThaiGPT is now OpenThai

The project was renamed in July 2026. Released models keep their original names, and this repository path is unchanged and permanent. → openthai.aieat.or.th

Other models in the family

Need	Model
⚖️ Thai law, statute citation, legal RAG	OpenThai 2.0 Legal 30B-A3B ← newest
🧠 Reasoning, maths, logic, code	OpenThaiGPT R1 32B
💬 General Thai chat and coding	OpenThaiGPT 1.6 72B
💻 Limited GPU	OpenThaiGPT 1.5 7B

⚡ Run it locally

ollama run openthai/openthai-1.6-72b

Official GGUF quants (Q4_K_M / Q5_K_M / Q8_0): openthaigpt/openthaigpt-1.6-72b-instruct-GGUF — ~48 GB RAM/VRAM in 4-bit. Step-by-step guide: openthai.aieat.or.th/ollama

Highlights

Advanced Thai language model with 72 billion parameters
Strong reasoning capabilities in both Thai and English
Improved performance over previous OpenThaiGPT versions
Balanced capabilities across mathematical, coding, and general language tasks
Enhanced understanding of Thai language and cultural context

Benchmark Results

Benchmarks	OpenThaiGPT 1.6 72b	OpenThaiGPT 1.5 7b	OpenThaiGPT 1.5 14b	OpenThaiGPT 1.5 72b	Typhoon2 Qwen2.5 7b	Typhoon2 Llama3.1 8b	Typhoon2 Llama3.1 70b	NECTEC Pathumma LLM Text 1.0.0 7b
AIME24-TH	6.67	0	0	6.67	3.33	3.33	13.33	0
AIME24	23.33	6.67	10	23.33	6.67	3.33	10	0
MATH500-TH	43.2	24.2	26.2	62	51.8	31	55.8	21.8
MATH500	82	40.4	47.4	83.2	65.4	49.6	67.4	42.8
LiveCodeBench-TH	32.43	22.52	21.62	12.61	9.91	8.11	27.03	0
LiveCodeBench	54.21	31.12	37.96	46.38	0.98	5.87	37.38	0
OpenThaiEval	78.7	64.5	71.26	77.16	64.76	56.63	72.54	65.27
Language Accuracy	98.2	97.6	98.4	99.4	99.4	98.6	99.8	98.6
AVERAGE	52.34	35.88	39.11	51.34	37.78	32.06	47.91	28.56

Recommended System Prompt

<No system prompt>

Model Technical Report

https://arxiv.org/abs/2504.01789

If OpenThaiGPT has been beneficial for your work, kindly consider citing it as follows:

@misc{yuenyong2025openthaigpt16r1thaicentric,
      title={OpenThaiGPT 1.6 and R1: Thai-Centric Open Source and Reasoning Large Language Models}, 
      author={Sumeth Yuenyong and Thodsaporn Chay-intr and Kobkrit Viriyayudhakorn},
      year={2025},
      eprint={2504.01789},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.01789}, 
}

How to use

Online Web Interface

https://chindax.iapp.co.th

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "openthaigpt/openthaigpt-1.6-72b"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "กรุงเทพมหานครคืออะไร"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384,
    temperature=0.6
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

vLLM

Install VLLM (https://github.com/vllm-project/vllm)
Run server

vllm serve openthaigpt/openthaigpt-1.6-72b --tensor-parallel-size 2

Note, change --tensor-parallel-size 2 to the amount of available GPU cards.

Run inference (CURL example)

curl -X POST 'http://127.0.0.1:8000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-d '{
  "model": "openthaigpt/openthaigpt-1.6-72b",
  "messages": [
    {
      "role": "user",
      "content": "กรุงเทพมหานครคืออะไร"
    }
  ],
  "max_tokens": 16384,
  "temperature": 0.6,
  "top_p": 0.95,
  "top_k": 40
}'

GPU Memory Requirements

Number of Parameters	FP 16 bits	8 bits (Quantized)	4 bits (Quantized)
72b	144 GB	72 GB	36 GB

Chat Template

{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n

Key Improvements in OpenThaiGPT 1.6 72b

Compared to OpenThaiGPT 1.5 72b, the 1.6 version shows:

Significantly better code reasoning performance in both Thai (LiveCodeBench-TH: 32.43 vs 12.61) and English (LiveCodeBench: 54.21 vs 46.38)
Comparable general language understanding with a high language accuracy of 98.2%
Better balanced capabilities across mathematical and coding tasks

This model maintains strong performance in general language tasks while improving specialized capabilities, making it suitable for a wide range of applications.