🇹🇭 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

⚡ 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

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

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

2. 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.

3. 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

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.

Licenses

• This model is available for Research and Commercial uses under the specified terms. Please see the LICENSE file for more information.

Supports

• Official website: https://openthai.aieat.or.th
• Facebook page: https://web.facebook.com/groups/openthaigpt
• A Discord server for discussion and support here
• E-mail: kobkrit@iapp.co.th

OpenThaiGPT Team

• Kobkrit Viriyayudhakorn (kobkrit@iapp.co.th / kobkrit@aieat.or.th)
• Sumeth Yuenyong (sumeth.yue@mahidol.edu)
• Thodsaporn Chay-intr (thodsaporn@iapp.co.th)

Sponsors

• ได้รับการสนับสนุน GPU Nvidia H100 x 8 ใบ จากบริษัท สยาม เอไอ คอร์เปอเรชั่น จำกัด: https://siam.ai/

• ได้รับทุนวิจัยสนับสนุนจากกองทุนส่งเสริมวิทยาศาสตร์ วิจัยและนวัตกรรม โดยหน่วยบริหารและจัดการทุนด้านการเพิ่มความสามารถในการแข่งขันของประเทศ (บพข.) ร่วมกับ บริษัท ไอแอพพ์เทคโนโลยี จำกัด ซึ่งมี สมาคมผู้ประกอบการปัญญาประดิษฐ์ประเทศไทย เป็นผู้ดำเนินงานโครงการ

Disclaimer: Provided responses are not guaranteed.