ðĨOpenThaiGPT 1.0.0 <8 Apr 2024>
ðđð OpenThaiGPT 1.0.0
ðđð OpenThaiGPT 7b, 13b, 70b Version 1.0.0 is an advanced 7, 13, 70-billion-parameter Thai language chat model based on LLaMA v2 released on April 8, 2024. It has been specifically fine-tuned for Thai instructions and enhanced by incorporating over 10,000 of the most commonly used Thai words into the large language model's (LLM) dictionary, significantly boosting its response speed.
Highlights
Leading-edge Thai language LLM, setting new benchmarks by achieving the highest average scores across several Thai language exams when compared to all other open-source Thai LLMs.
The First 70b Thai opensource LLM, achieving the higher Thai exams than OpenAI GPT 3.5, Google Gemini, and Claude 3 Haiku.
Support for extended conversations across multiple turns.
Support the use case of Retrieval Augmented Generation (RAG) for enriched response generation.
Generation speeds increased by tenfold, thanks to the addition of 10,000 frequently used Thai words to the model's dictionary.
Pretrained upon a foundation of more than 65 billion Thai language words and meticulously fine-tuned with over 1 million Thai instruction examples.
Capable of understanding and processing input contexts of up to 4096 Thai words, allowing for detailed and complex instructions.
Download Models from Huggingface
7b - https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat 7b (GGUF) - https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat-gguf 13b - https://huggingface.co/openthaigpt/openthaigpt-1.0.0-13b-chat 70b - https://huggingface.co/openthaigpt/openthaigpt-1.0.0-70b-chat
Pipeline
https://colab.research.google.com/drive/1w1giDWhmq3WIUCK4AISFJtGIqiPDtRSC?usp=sharing
Benchmark by OpenThaiGPT Eval
** Please take a look at OTG 7b, 13b and 70b (April 2024)
for this model's evaluation result.
Exams
OTG 7b (Aug 2023)
OTG 13b (Dec 2023)
OTG 7b (April 2024)
OTG 13b (April 2024)
OTG 70b (April 2024)
SeaLLM 7b v1
SeaLLM 7b v2
SeaLion 7b
WanchanGLM 7b
Sailor-7b-Chat
TyphoonGPT 7b Instruct
GPT3.5
GPT4
Gemini Pro
Gemini 1.5
Claude 3 Haiku
Claude 3 Sonnet
Claude 3 Opus
A-Level
17.50%
34.17%
25.00%
30.83%
45.83%
18.33%
34.17%
21.67%
17.50%
40.00%
37.50%
38.33%
65.83%
56.67%
55.83%
58.33%
59.17%
77.50%
TGAT
24.00%
22.00%
22.00%
36.00%
36.00%
14.00%
28.00%
24.00%
16.00%
34.00%
30.00%
28.00%
44.00%
22.00%
28.00%
36.00%
34.00%
46.00%
TPAT1
22.50%
47.50%
42.50%
27.50%
62.50%
22.50%
27.50%
22.50%
17.50%
40.00%
47.50%
45.00%
52.50%
52.50%
50.00%
52.50%
50.00%
62.50%
thai_investment_consultant_exams
8.00%
28.00%
76.00%
84.00%
68.00%
16.00%
28.00%
24.00%
16.00%
24.00%
32.00%
40.00%
64.00%
52.00%
32.00%
44.00%
64.00%
72.00%
facebook_beleble_tha_200
25.00%
45.00%
34.50%
39.50%
70.00%
13.50%
51.00%
27.00%
24.50%
63.00%
51.50%
50.00%
72.50%
65.00%
74.00%
63.50%
77.00%
90.00%
xcopa_th_200
45.00%
56.50%
49.50%
51.50%
74.50%
26.50%
47.00%
51.50%
48.50%
68.50%
65.00%
64.00%
82.00%
68.00%
74.00%
64.00%
80.00%
86.00%
xnli2.0_th_200
33.50%
34.50%
39.50%
31.00%
47.00%
21.00%
43.00%
37.50%
33.50%
16.00%
20.00%
50.00%
69.00%
53.00%
54.50%
50.00%
68.00%
68.50%
ONET M3
17.85%
38.86%
34.11%
39.36%
56.15%
15.58%
23.92%
21.79%
19.56%
21.37%
28.03%
37.91%
49.97%
55.99%
57.41%
52.73%
40.60%
63.87%
ONET M6
21.14%
28.87%
22.53%
23.32%
42.85%
15.09%
19.48%
16.96%
20.67%
28.64%
27.46%
34.44%
46.29%
45.53%
50.23%
34.79%
38.49%
48.56%
AVERAGE SCORE
23.83%
37.27%
38.40%
40.33%
55.87%
18.06%
33.56%
27.44%
23.75%
37.28%
37.67%
43.07%
60.68%
52.30%
52.89%
50.65%
56.81%
68.32%
Thai language multiple choice exams, Test on unseen test sets, Zero-shot learning. Benchmark source code and exams information: https://github.com/OpenThaiGPT/openthaigpt_eval
(Updated on: 7 April 2024)
Licenses
Source Code: License Apache Software License 2.0. Weight: Research and Commercial uses.
Sponsors
Supports
Official website: https://openthaigpt.aieat.or.th
Facebook page: https://web.facebook.com/groups/openthaigpt
A Discord server for discussion and support here
E-mail: [email protected]
Prompt Format
Prompt format is based on Llama2 with a small modification (Adding "###" to specify the context part)
<s>[INST] <<SYS>
{system_prompt}
<</SYS>>
{human_turn1}###{context_turn1} [/INST]{assistant_turn1}</s><s>{human_turn2}###{context_turn2} [/INST] ...
System prompt:
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
Examples
Single Turn Conversation Example
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ [/INST]
Single Turn Conversation with Context (RAG) Example
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļāļĢāļļāļāđāļāļāļĄāļĩāļāļ·āđāļāļāļĩāđāđāļāđāļēāđāļĢāđ###āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āđāļāđāļāđāļĄāļ·āļāļāļŦāļĨāļ§āļ āļāļāļĢāđāļĨāļ°āļĄāļŦāļēāļāļāļĢāļāļĩāđāļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļĄāļēāļāļāļĩāđāļŠāļļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļĄāļĩāļāļ·āđāļāļāļĩāđāļāļąāđāļāļŦāļĄāļ 1,568.737 āļāļĢ.āļāļĄ. āļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļāļēāļĄāļāļ°āđāļāļĩāļĒāļāļĢāļēāļĐāļāļĢāļāļ§āđāļē 8 āļĨāđāļēāļāļāļ [/INST]
Multi Turn Conversation Example
First turn
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ [/INST]
Second turn
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ [/INST]āļŠāļ§āļąāļŠāļāļĩāļāđāļ° āļĄāļĩāļāļģāļāļēāļĄāļāļ°āđāļĢ āļāļēāļĄāđāļāđāđāļĨāļĒ</s><s>āļāļāļŠāļđāļāļĢāļāļģāļŠāđāļĄāļāļģāļŦāļāđāļāļĒ [/INST]
Third turn
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ [/INST]āļŠāļ§āļąāļŠāļāļĩāļāđāļ° āļĄāļĩāļāļģāļāļēāļĄāļāļ°āđāļĢ āļāļēāļĄāđāļāđāđāļĨāļĒ</s><s>āļāļāļŠāļđāļāļĢāļāļģāļŠāđāļĄāļāļģāļŦāļāđāļāļĒ [/INST]āđāļāđāđāļĨāļĒāļāđāļ° āļŠāđāļĄāļāļģāđāļāđāļāđāļĄāļāļđāļāļĩāđāļāļģāļāđāļēāļĒāđāļĨāļ°āļāļĢāđāļāļĒ āļĄāļēāđāļĢāļīāđāļĄāļāļąāļāđāļĨāļĒāļāļ°āļāļ°</s><s>āđāļĢāļīāđāļĄāđāļāđāđāļĨāļĒ [/INST]
Fourth turn
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ [/INST]āļŠāļ§āļąāļŠāļāļĩāļāđāļ° āļĄāļĩāļāļģāļāļēāļĄāļāļ°āđāļĢ āļāļēāļĄāđāļāđāđāļĨāļĒ</s><s>āļāļāļŠāļđāļāļĢāļāļģāļŠāđāļĄāļāļģāļŦāļāđāļāļĒ [/INST]āđāļāđāđāļĨāļĒāļāđāļ° āļŠāđāļĄāļāļģāđāļāđāļāđāļĄāļāļđāļāļĩāđāļāļģāļāđāļēāļĒāđāļĨāļ°āļāļĢāđāļāļĒ āļĄāļēāđāļĢāļīāđāļĄāļāļąāļāđāļĨāļĒāļāļ°āļāļ°</s><s>āđāļĢāļīāđāļĄāđāļāđāđāļĨāļĒ [/INST]
āļāļģāđāļāļĒāđāļāđāļāđāļĄāļāļđāļāļĩāđāđāļāļĢāđ āļāđāļāļļāđāļāđāļāļĒ āļāđāļ§āļĒāļĢāļŠāļāļēāļāļīāļāļĩāđāļāļĨāļĄāļāļĨāđāļāļĄ āļŦāļ§āļēāļ āđāļāļĢāļĩāđāļĒāļ§ āđāļāđāļĄ āđāļāđāļ āļāļĢāļāļĢāļŠ āļ§āļąāļāļāļĩāđāđāļĢāļēāļāļ°āļĄāļēāļāļģāļŠāđāļĄāļāļģāļāđāļ§āļĒāļāļąāļāļāđāļ° āļŠāļīāđāļāđāļĢāļāļāļĩāđāļāđāļāļāļĄāļĩāļāļ·āļāđāļāļĢāļ·āđāļāļāļāļĢāļļāļāļāļąāļāļāļĩāđ
- āļĄāļ°āļĨāļ°āļāļāļāļīāļ 1 āļĨāļđāļ
- āļāļļāđāļāđāļŦāđāļ 1/2 āļāđāļ§āļĒ
- āļāļĢāļ°āđāļāļĩāļĒāļĄ 3 āļāļĨāļĩāļ
- āļāļĢāļīāļāļāļĩāđāļŦāļāļđ 3 āđāļĄāđāļ
- āļāđāļģāļāļēāļĨāļāļĩāđāļ 1 āļāđāļāļāđāļāđāļ°
- āļāđāļģāļāļĨāļē 2 āļāđāļāļāđāļāđāļ°
- āļĄāļ°āļāļēāļ§ 1 āļĨāļđāļ
- āļāļēāļāļēāļ§āļāļąāđāļ§ 1/4 āļāđāļ§āļĒ
āļ§āļīāļāļĩāļāļģāļĄāļĩāļāļąāļāļāļĩāđāļāđāļ°
1. āđāļĢāļīāđāļĄāļāļēāļāļĨāđāļēāļāļĄāļ°āļĨāļ°āļāļāđāļŦāđāļŠāļ°āļāļēāļ āđāļĨāđāļ§āđāļāđāļĄāļĩāļāļāļāļāđāļāļĨāļ·āļāļ āđāļāļēāđāļŠāđāļāļāļ āļŦāļąāđāļāđāļāđāļāđāļŠāđāļāļāļēāļāđ āđāļāļĢāļĩāļĒāļĄāđāļ§āđ
2. āļāļģāļāļļāđāļāđāļŦāđāļāđāļŦāđāļĨāļ°āđāļāļĩāļĒāļ āđāļĨāđāļ§āļāļąāļāļāļķāđāļāļāļąāļāđāļ§āđ
3. āđāļāđāļāļĢāļāļŦāļīāļāļŦāļĢāļ·āļāđāļāļĢāļ·āđāļāļāļāļąāđāļ āļāļāļāļĢāļīāļāļāļĩāđāļŦāļāļđāļāļąāļāļāļĢāļ°āđāļāļĩāļĒāļĄāđāļŦāđāļĨāļ°āđāļāļĩāļĒāļ
4. āđāļŠāđāļāļļāđāļāđāļŦāđāļāļāļĩāđāļāļģāđāļĨāđāļ§āļĨāļāđāļāļāļŠāļĄ āļāļēāļĄāļāđāļ§āļĒāļāđāļģāļāļēāļĨāļāļĩāđāļ āļāđāļģāļāļĨāļē āļĄāļ°āļāļēāļ§ āđāļĨāļ°āđāļŠāđāļāļĄāļ°āļĨāļ°āļāļ āļāļĨāļļāļāđāļāļĨāđāļēāđāļŦāđāđāļāđāļēāļāļąāļ
5. āļāļīāļĄāļĢāļŠāđāļŦāđāđāļāđāļĢāļŠāļŦāļ§āļēāļ āđāļāļĢāļĩāđāļĒāļ§ āđāļāđāļĄ āđāļāđāļ āļāļēāļāļāļąāđāļāļāļąāļāļāļķāđāļāđāļŠāļīāļĢāđāļāļāļĢāđāļāļĄāļāļąāļāļŠāļ āļāļēāļāļī āļāļ°āļŦāļĨāđāļģāļāļĨāļĩ āļāļąāđāļ§āļāļāļ āđāļāļĢāļāļ āļāļąāļāļāļļāđāļ</s><s>āļāļāļāļāļļāļāļāļĢāļąāļ [/INST]
Multi Turn Conversation with Context (RAG) Example
<s>[INST] <<SYS>
You are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ
<</SYS>>
āļāļĢāļļāļāđāļāļāļĄāļĩāļāļ·āđāļāļāļĩāđāđāļāđāļēāđāļĢāđ###āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āđāļāđāļāđāļĄāļ·āļāļāļŦāļĨāļ§āļ āļāļāļĢāđāļĨāļ°āļĄāļŦāļēāļāļāļĢāļāļĩāđāļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļĄāļēāļāļāļĩāđāļŠāļļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļĄāļĩāļāļ·āđāļāļāļĩāđāļāļąāđāļāļŦāļĄāļ 1,568.737 āļāļĢ.āļāļĄ. āļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļāļēāļĄāļāļ°āđāļāļĩāļĒāļāļĢāļēāļĐāļāļĢāļāļ§āđāļē 8 āļĨāđāļēāļāļāļ [/INST]
āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļĄāļĩāļāļ·āđāļāļāļĩāđāļāļąāđāļāļŦāļĄāļ 1,568.737 āļāļĢ.āļāļĄ.</s><s>āđāļĨāļ°āļāļĢāļ°āļāļēāļāļĢāļĨāđāļ° [/INST]
How to use
Huggingface
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Ensure CUDA is available
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")
# Init Model
model_path="openthaigpt/openthaigpt-1.0.0-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.float16)
model.to(device)
# Prompt
prompt = "āļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ OpenThaiGPT"
llama_prompt = f"<s>[INST] <<SYS>>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ<</SYS>>\n\n{prompt} [/INST]"
inputs = tokenizer.encode(llama_prompt, return_tensors="pt")
inputs = inputs.to(device)
# Generate
outputs = model.generate(inputs, max_length=512, num_return_sequences=1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
vLLM
Install VLLM (https://github.com/vllm-project/vllm)
Run server
python -m vllm.entrypoints.api_server --model /path/to/model --tensor-parallel-size num_gpus
Run inference (CURL example)
curl --request POST \
--url http://localhost:8000/generate \
--header "Content-Type: application/json" \
--data '{"prompt": "<s>[INST] <<SYS>>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ\n<</SYS>>\n\nāļāļĒāļēāļāļĨāļāļāļ§āļēāļĄāļāđāļ§āļāļāđāļāļāļāļģāļāļĒāđāļēāļāđāļĢ [/INST]","use_beam_search": false, "temperature": 0.1, "max_tokens": 512, "top_p": 0.75, "top_k": 40, "frequency_penalty": 0.3 "stop": "</s>"}'
LlamaCPP (for GGUF)
Build and Install LlamaCPP (LLAMA_CUBLAS=1 is for GPU inference)
git clone https://github.com/ggerganov/llama.cpp.git \
&& cd llama.cpp \
&& make -j LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=all
Run server
./server -m /path/to/ggml-model-f16.gguf -c 3072 -ngl 81 -ts 1,1 --host 0.0.0.0
Run inference (CURL example)
curl --location 'http://localhost:8000/completion' \
--header 'Content-Type: application/json' \
--data '{
"prompt":"<s>[INST] <<SYS>>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄ āļāļāļāļāļāļāļģāļāļēāļĄāļāļĒāđāļēāļāļāļđāļāļāđāļāļāđāļĨāļ°āļĄāļĩāļāļĢāļ°āđāļĒāļāļāđāļāļĩāđāļŠāļļāļ friendly\n\n<<SYS>>\n\nāļāļĒāļēāļāļĨāļāļāļ§āļēāļĄāļāđāļ§āļāļāđāļāļāļāļģāļāļĒāđāļēāļāđāļĢ [/INST]",
"max_tokens": 512,
"stop":"</s>"
}'
GPU Memory Requirements
Number of Parameters
FP 16 bits
8 bits (Quantized)
4 bits (Quantized)
Example Graphic Card for 4 bits
7b
24 GB
12 GB
6 GB
Nvidia RTX 4060 8GB
13b
48 GB
24 GB
12 GB
Nvidia RTX 4070 16GB
70b
192 GB
96 GB
48 GB
Nvidia RTX 4090 24GB x 2 cards
Authors
Kobkrit Viriyayudhakorn ([email protected])
Sumeth Yuenyong ([email protected])
Thaweewat Rugsujarit ([email protected])
Jillaphat Jaroenkantasima ([email protected])
Norapat Buppodom ([email protected])
Koravich Sangkaew ([email protected])
Peerawat Rojratchadakorn ([email protected])
Surapon Nonesung ([email protected])
Chanon Utupon ([email protected])
Sadhis Wongprayoon ([email protected])
Nucharee Thongthungwong ([email protected])
Chawakorn Phiantham ([email protected])
Patteera Triamamornwooth ([email protected])
Nattarika Juntarapaoraya ([email protected])
Kriangkrai Saetan ([email protected])
Pitikorn Khlaisamniang ([email protected])
Disclaimer: Provided responses are not guaranteed.
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