metadata

license: other
license_name: qwen
language:
  - th
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - openthaigpt
  - qwen
model-index:
  - name: OpenThaiGPT1.5-72b
    results:
      - task:
          type: text-generation
        dataset:
          name: ThaiExam
          type: multiple_choices
        metrics:
          - name: Thai Exam(Acc)
            type: accuracy
            value: 63.89
        source:
          name: 🇹🇭 Thai LLM Leaderboard
          url: https://huggingface.co/spaces/ThaiLLM-Leaderboard/leaderboard
      - task:
          type: text-generation
        dataset:
          name: M3Exam
          type: multiple_choices
        metrics:
          - name: M3Exam(Acc)
            type: Accuracy
            value: 70.39
        source:
          name: 🇹🇭 Thai LLM Leaderboard
          url: https://huggingface.co/spaces/ThaiLLM-Leaderboard/leaderboard

🇹🇭 OpenThaiGPT 72b 1.5 Instruct

More Info

🇹🇭 OpenThaiGPT 72b Version 1.5 is an advanced 72-billion-parameter Thai language chat model based on Qwen v2.5 released on September 30, 2024. It has been specifically fine-tuned on over 2,000,000 Thai instruction pairs and is capable of answering Thai-specific domain questions.

Online Demo:

https://demo72b.aieat.or.th/

Example code for API Calling

https://github.com/OpenThaiGPT/openthaigpt1.5_api_examples

Highlights

State-of-the-art Thai language LLM, achieving the highest average scores across various Thai language exams compared to other open-source Thai LLMs.
Multi-turn conversation support for extended dialogues.
Retrieval Augmented Generation (RAG) compatibility for enhanced response generation.
Impressive context handling: Processes up to 131,072 tokens of input and generates up to 8,192 tokens, enabling detailed and complex interactions.
Tool calling support: Enables users to efficiently call various functions through intelligent responses.

Benchmark on OpenThaiGPT Eval

** Please take a look at openthaigpt/openthaigpt1.5-72b-instruct for this model's evaluation result.

Exam names	scb10x/llama-3-typhoon-v1.5x-70b-instruct	meta-llama/Llama-3.1-70B-Instruct	Qwen/Qwen2.5-72B-Instruct	openthaigpt/openthaigpt1.5-72b-instruct
01_a_level	59.17%	61.67%	75.00%	76.67%
02_tgat	46.00%	40.00%	48.00%	46.00%
03_tpat1	52.50%	50.00%	55.00%	55.00%
04_investment_consult	60.00%	52.00%	80.00%	72.00%
05_facebook_beleble_th_200	87.50%	88.00%	90.00%	90.00%
06_xcopa_th_200	84.50%	85.50%	90.00%	90.50%
07_xnli2.0_th_200	62.50%	63.00%	65.50%	70.50%
08_onet_m3_thai	76.00%	56.00%	76.00%	84.00%
09_onet_m3_social	95.00%	95.00%	90.00%	95.00%
10_onet_m3_math	43.75%	25.00%	37.50%	37.50%
11_onet_m3_science	53.85%	61.54%	65.38%	73.08%
12_onet_m3_english	93.33%	93.33%	96.67%	96.67%
13_onet_m6_thai	55.38%	60.00%	60.00%	56.92%
14_onet_m6_math	41.18%	58.82%	23.53%	41.18%
15_onet_m6_social	67.27%	76.36%	63.64%	65.45%
16_onet_m6_science	50.00%	57.14%	64.29%	67.86%
17_onet_m6_english	73.08%	82.69%	86.54%	90.38%
Micro Average	69.97%	71.09%	75.02%	76.73%

Thai language multiple choice exams, Test on unseen test set, Zero-shot learning. Benchmark source code and exams information: https://github.com/OpenThaiGPT/openthaigpt_eval

(Updated on: 30 September 2024)

Benchmark on scb10x/thai_exam

Models	Thai Exam (Acc)
api/claude-3-5-sonnet-20240620	69.2
openthaigpt/openthaigpt1.5-72b-instruct*	64.07
api/gpt-4o-2024-05-13	63.89
hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4	63.54
openthaigpt/openthaigpt1.5-14b-instruct*	59.65
scb10x/llama-3-typhoon-v1.5x-70b-instruct	58.76
Qwen/Qwen2-72B-Instruct	58.23
meta-llama/Meta-Llama-3.1-70B-Instruct	58.23
Qwen/Qwen2.5-14B-Instruct	57.35
api/gpt-4o-mini-2024-07-18	54.51
openthaigpt/openthaigpt1.5-7b-instruct*	52.04
SeaLLMs/SeaLLMs-v3-7B-Chat	51.33
openthaigpt/openthaigpt-1.0.0-70b-chat	50.09

* Evaluated by OpenThaiGPT team using scb10x/thai_exam.

(Updated on: 13 October 2024)

Licenses

Built with Qwen
Qwen License: Allow Research and Commercial uses but if your user base exceeds 100 million monthly active users, you need to negotiate a separate commercial license. Please see LICENSE file for more information.

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: kobkrit@aieat.or.th

Prompt Format

Prompt format is based on ChatML.

<|im_start|>system\n{sytem_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n

System prompt:

คุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์

Examples

Single Turn Conversation Example

<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n

Single Turn Conversation with Context (RAG) Example

<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร เป็นเมืองหลวง นครและมหานครที่มีประชากรมากที่สุดของประเทศไทย กรุงเทพมหานครมีพื้นที่ทั้งหมด 1,568.737 ตร.กม. มีประชากรตามทะเบียนราษฎรกว่า 8 ล้านคน\nกรุงเทพมหานครมีพื้นที่เท่าไร่<|im_end|>\n<|im_start|>assistant\n

Multi Turn Conversation Example

First turn

<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n

Second turn

<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\nสวัสดีครับ ยินดีต้อนรับครับ คุณต้องการให้ฉันช่วยอะไรครับ?<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร ชื่อเต็มยาวๆคืออะไร<|im_end|>\n<|im_start|>assistant\n

Result

<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\nสวัสดีครับ ยินดีต้อนรับครับ คุณต้องการให้ฉันช่วยอะไรครับ?<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร ชื่อเต็มยาวๆคืออะไร<|im_end|>\n<|im_start|>assistant\nชื่อเต็มของกรุงเทพมหานครคือ \"กรุงเทพมหานคร อมรรัตนโกสินทร์ มหินทรายุธยา มหาดิลกภพ นพรัตนราชธานีบูรีรมย์ อุดมราชนิเวศน์มหาสถาน อมรพิมานอวตารสถิต สักกะทัตติยวิษณุกรรมประสิทธิ์\"

How to use

Free API Service (hosted by Siam.Ai and Float16.cloud)

Siam.AI

curl https://api.aieat.or.th/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dummy" \
  -d '{
    "model": ".",
    "prompt": "<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nกรุงเทพมหานครคืออะไร<|im_end|>\n<|im_start|>assistant\n",
    "max_tokens": 512,
    "temperature": 0.7,
    "top_p": 0.8,
    "top_k": 40,
    "stop": ["<|im_end|>"]
  }'

Float16

curl -X POST https://api.float16.cloud/dedicate/78y8fJLuzE/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer float16-AG0F8yNce5s1DiXm1ujcNrTaZquEdaikLwhZBRhyZQNeS7Dv0X" \
  -d '{
    "model": "openthaigpt/openthaigpt1.5-7b-instruct",
    "messages": [
      {
        "role": "system",
        "content": "คุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์"
      },
      {
        "role": "user",
        "content": "สวัสดี"
      }
    ]
   }'

OpenAI Client Library (Hosted by VLLM, please see below.)

import openai

# Configure OpenAI client to use vLLM server
openai.api_base = "http://127.0.0.1:8000/v1"
openai.api_key = "dummy"  # vLLM doesn't require a real API key

prompt = "<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nกรุงเทพมหานครคืออะไร<|im_end|>\n<|im_start|>assistant\n"

try:
    response = openai.Completion.create(
        model=".",  # Specify the model you're using with vLLM
        prompt=prompt,
        max_tokens=512,
        temperature=0.7,
        top_p=0.8,
        top_k=40,
        stop=["<|im_end|>"]
    )
    print("Generated Text:", response.choices[0].text)
except Exception as e:
    print("Error:", str(e))

Huggingface

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "openthaigpt/openthaigpt1.5-72b-instruct"

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

prompt = "ประเทศไทยคืออะไร"
messages = [
    {"role": "system", "content": "คุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์"},
    {"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=512
)
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/openthaigpt1.5-72b-instruct --tensor-parallel-size 4

Note, change --tensor-parallel-size 4 to the amount of available GPU cards.

If you wish to enable tool calling feature, add --enable-auto-tool-choice --tool-call-parser hermes into command. e.g.,

vllm serve openthaigpt/openthaigpt1.5-72b-instruct --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser hermes

Run inference (CURL example)

curl -X POST 'http://127.0.0.1:8000/v1/completions' \
-H 'Content-Type: application/json' \
-d '{
  "model": ".",
  "prompt": "<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n",
  "max_tokens": 512,
  "temperature": 0.7,
  "top_p": 0.8,
  "top_k": 40,
  "stop": ["<|im_end|>"]
}'

Processing Long Texts

The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For supported frameworks, you could add the following to config.json to enable YaRN:

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

Tool Calling

The Tool Calling feature in OpenThaiGPT 1.5 enables users to efficiently call various functions through intelligent responses. This includes making external API calls to retrieve real-time data, such as current temperature information, or predicting future data simply by submitting a query. For example, a user can ask OpenThaiGPT, “What is the current temperature in San Francisco?” and the AI will execute a pre-defined function to provide an immediate response without the need for additional coding. This feature also allows for broader applications with external data sources, including the ability to call APIs for services such as weather updates, stock market information, or data from within the user’s own system.

Example:

import openai

def get_temperature(location, date=None, unit="celsius"):
    """Get temperature for a location (current or specific date)."""
    if date:
        return {"temperature": 25.9, "location": location, "date": date, "unit": unit}
    return {"temperature": 26.1, "location": location, "unit": unit}

tools = [
    {
        "name": "get_temperature",
        "description": "Get temperature for a location (current or by date).",
        "parameters": {
            "location": "string", "date": "string (optional)", "unit": "enum [celsius, fahrenheit]"
        },
    }
]

messages = [{"role": "user", "content": "อุณหภูมิที่ San Francisco วันนี้ีและพรุ้่งนี้คือเท่าไร่?"}]

# Simulated response flow using OpenThaiGPT Tool Calling
response = openai.ChatCompletion.create(
    model=".", messages=messages, tools=tools, temperature=0.7, max_tokens=512
)

print(response)

Full example: https://github.com/OpenThaiGPT/openthaigpt1.5_api_examples/blob/main/api_tool_calling_powered_by_siamai.py

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
72b	192 GB	96 GB	48 GB	Nvidia RTX 4090 24GB x 2 cards

OpenThaiGPT Team

Sumeth Yuenyong (sumeth.yue@mahidol.edu)
Kobkrit Viriyayudhakorn (kobkrit@aieat.or.th)
Apivadee Piyatumrong (apivadee.piy@nectec.or.th)
Jillaphat Jaroenkantasima (autsadang41@gmail.com)
Thaweewat Rugsujarit (thaweewr@scg.com)
Norapat Buppodom (new@norapat.com)
Koravich Sangkaew (kwankoravich@gmail.com)
Peerawat Rojratchadakorn (peerawat.roj@gmail.com)
Surapon Nonesung (nonesungsurapon@gmail.com)
Chanon Utupon (chanon.utupon@gmail.com)
Sadhis Wongprayoon (sadhis.tae@gmail.com)
Nucharee Thongthungwong (nuchhub@hotmail.com)
Chawakorn Phiantham (mondcha1507@gmail.com)
Patteera Triamamornwooth (patt.patteera@gmail.com)
Nattarika Juntarapaoraya (natt.juntara@gmail.com)
Kriangkrai Saetan (kraitan.ss21@gmail.com)
Pitikorn Khlaisamniang (pitikorn32@gmail.com)

Citation

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

Bibtex

@misc{yuenyong2024openthaigpt15thaicentricopen,
      title={OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model}, 
      author={Sumeth Yuenyong and Kobkrit Viriyayudhakorn and Apivadee Piyatumrong and Jillaphat Jaroenkantasima},
      year={2024},
      eprint={2411.07238},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.07238}, 
}

APA Style (for TXT, MS Word)

Yuenyong, S., Viriyayudhakorn, K., Piyatumrong, A., & Jaroenkantasima, J. (2024). OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model. arXiv [Cs.CL]. Retrieved from http://arxiv.org/abs/2411.07238

Disclaimer: Provided responses are not guaranteed.

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openthaigpt1.5-72b-instruct