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🌟 OpenThaiGPT 1.6 72b

OpenThaiGPT 1.6 72b is a 72-billion-parameter Thai language model designed for general-purpose tasks with strong analytical thinking capabilities. Building on the foundation of OpenThaiGPT 1.5, this latest release demonstrates improved performance across a wide range of benchmarks, particularly in coding and general language tasks.

Download the model​

OpenThaiGPT 1.6 72b — Hugging Facehuggingface.co

openthaigpt/openthaigpt-1.6-72b-instruct

Highlights​

  • Advanced Thai language model with 72 billion parameters
  • Strong analytical thinking capabilities in both Thai and English
  • Improved performance compared to previous OpenThaiGPT releases
  • Balanced capabilities across mathematics, coding, and general language tasks
  • Deep understanding of the Thai language and cultural context

Key improvements over 1.5​

Compared to OpenThaiGPT 1.5 72b, version 1.6 demonstrates:

  • Substantially improved coding performance in both Thai (LiveCodeBench-TH: 32.43 vs 12.61) and English (LiveCodeBench: 54.21 vs 46.38)
  • Comparable general language understanding with language accuracy as high as 98.2%
  • Better balanced capabilities between mathematics and coding tasks

Benchmark results​

BenchmarksOpenThaiGPT 1.6 72bOpenThaiGPT 1.5 7bOpenThaiGPT 1.5 14bOpenThaiGPT 1.5 72bTyphoon2 Qwen2.5 7bTyphoon2 Llama3.1 8bTyphoon2 Llama3.1 70bNECTEC Pathumma LLM Text 1.0.0 7b
AIME24-TH6.67006.673.333.3313.330
AIME2423.336.671023.336.673.33100
MATH500-TH43.224.226.26251.83155.821.8
MATH5008240.447.483.265.449.667.442.8
LiveCodeBench-TH32.4322.5221.6212.619.918.1127.030
LiveCodeBench54.2131.1237.9646.380.985.8737.380
OpenThaiEval78.764.571.2677.1664.7656.6372.5465.27
Language Accuracy98.297.698.499.499.498.699.898.6
AVERAGE52.3435.8839.1151.3437.7832.0647.9128.56

Technical report​

OpenThaiGPT 1.6 and R1 Technical Report — arXivarxiv.org

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​

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=8192,
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 (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.
  1. 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": 4096,
"temperature": 0.6,
"top_p": 0.95,
"top_k": 40
}'

GPU memory requirements​

Number of ParametersFP 16 bits8 bits (Quantized)4 bits (Quantized)
72b144 GB72 GB36 GB

License​

  • This model is available for research and commercial use under the terms of the Qwen2.5 license agreement. Please refer to the LICENSE file for more information.

Support​

Disclaimer: Provided responses are not guaranteed.