QuantFactory/openthaigpt1.5-7b-instruct-GGUF

This is quantized version of openthaigpt/openthaigpt1.5-7b-instruct created using llama.cpp

Original Model Card

🇹🇭 OpenThaiGPT 7b 1.5 Instruct

🇹🇭 OpenThaiGPT 7b Version 1.5 is an advanced 7-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/openthaigpt1.5-7b-instruct for this model's evaluation result.

Exam names	scb10x/llama-3-typhoon-v1.5x-8b-instruct	meta-llama/Llama-3.1-7B-Instruct	Qwen/Qwen2.5-7B-Instruct_stat	openthaigpt/openthaigpt1.5-7b
01_a_level	46.67%	47.50%	58.33%	60.00%
02_tgat	32.00%	36.00%	32.00%	36.00%
03_tpat1	52.50%	55.00%	57.50%	57.50%
04_investment_consult	56.00%	48.00%	68.00%	76.00%
05_facebook_beleble_th_200	78.00%	73.00%	79.00%	81.00%
06_xcopa_th_200	79.50%	69.00%	80.50%	81.00%
07_xnli2.0_th_200	56.50%	55.00%	53.00%	54.50%
08_onet_m3_thai	48.00%	32.00%	72.00%	64.00%
09_onet_m3_social	75.00%	50.00%	90.00%	80.00%
10_onet_m3_math	25.00%	18.75%	31.25%	31.25%
11_onet_m3_science	46.15%	42.31%	46.15%	46.15%
12_onet_m3_english	70.00%	76.67%	86.67%	83.33%
13_onet_m6_thai	47.69%	29.23%	46.15%	53.85%
14_onet_m6_math	29.41%	17.65%	29.41%	29.41%
15_onet_m6_social	50.91%	43.64%	56.36%	58.18%
16_onet_m6_science	42.86%	32.14%	57.14%	57.14%
17_onet_m6_english	65.38%	71.15%	78.85%	80.77%
Micro Average	60.65%	55.60%	64.41%	65.78%

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
Qwen/Qwen2-72B-Instruct	58.23
meta-llama/Meta-Llama-3.1-70B-Instruct	58.23
scb10x/llama-3-typhoon-v1.5x-70b-instruct	58.76
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.

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.

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

Authors

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)

Disclaimer: Provided responses are not guaranteed.

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for QuantFactory/openthaigpt1.5-7b-instruct-GGUF

YaRN: Efficient Context Window Extension of Large Language Models

Paper • 2309.00071 • Published Aug 31, 2023 • 79

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openthaigpt1.5-7b-instruct-GGUF