OpenThaiGPT 1.5
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Latest updated OpenThaiGPT 1.5
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πΉπ OpenThaiGPT 72b 1.5 Instruct
πΉπ 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:
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.
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: 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:
ΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ
Examples
Single Turn Conversation Example
<|im_start|>system\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ<|im_end|>\n<|im_start|>assistant\n
Single Turn Conversation with Context (RAG) Example
<|im_start|>system\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ ΰΉΰΈΰΉΰΈΰΉΰΈ‘ΰΈ·ΰΈΰΈΰΈ«ΰΈ₯ΰΈ§ΰΈ ΰΈΰΈΰΈ£ΰΉΰΈ₯ΰΈ°ΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈΰΈ΅ΰΉΰΈ‘ΰΈ΅ΰΈΰΈ£ΰΈ°ΰΈΰΈ²ΰΈΰΈ£ΰΈ‘ΰΈ²ΰΈΰΈΰΈ΅ΰΉΰΈͺΰΈΈΰΈΰΈΰΈΰΈΰΈΰΈ£ΰΈ°ΰΉΰΈΰΈ¨ΰΉΰΈΰΈ’ ΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈ‘ΰΈ΅ΰΈΰΈ·ΰΉΰΈΰΈΰΈ΅ΰΉΰΈΰΈ±ΰΉΰΈΰΈ«ΰΈ‘ΰΈ 1,568.737 ΰΈΰΈ£.ΰΈΰΈ‘. ΰΈ‘ΰΈ΅ΰΈΰΈ£ΰΈ°ΰΈΰΈ²ΰΈΰΈ£ΰΈΰΈ²ΰΈ‘ΰΈΰΈ°ΰΉΰΈΰΈ΅ΰΈ’ΰΈΰΈ£ΰΈ²ΰΈ©ΰΈΰΈ£ΰΈΰΈ§ΰΉΰΈ² 8 ΰΈ₯ΰΉΰΈ²ΰΈΰΈΰΈ\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈ‘ΰΈ΅ΰΈΰΈ·ΰΉΰΈΰΈΰΈ΅ΰΉΰΉΰΈΰΉΰΈ²ΰΉΰΈ£ΰΉ<|im_end|>\n<|im_start|>assistant\n
Multi Turn Conversation Example
First turn
<|im_start|>system\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ<|im_end|>\n<|im_start|>assistant\n
Second turn
<|im_start|>system\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ<|im_end|>\n<|im_start|>assistant\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ ΰΈ’ΰΈ΄ΰΈΰΈΰΈ΅ΰΈΰΉΰΈΰΈΰΈ£ΰΈ±ΰΈΰΈΰΈ£ΰΈ±ΰΈ ΰΈΰΈΈΰΈΰΈΰΉΰΈΰΈΰΈΰΈ²ΰΈ£ΰΉΰΈ«ΰΉΰΈΰΈ±ΰΈΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈ°ΰΉΰΈ£ΰΈΰΈ£ΰΈ±ΰΈ?<|im_end|>\n<|im_start|>user\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ ΰΈΰΈ·ΰΉΰΈΰΉΰΈΰΉΰΈ‘ΰΈ’ΰΈ²ΰΈ§ΰΉΰΈΰΈ·ΰΈΰΈΰΈ°ΰΉΰΈ£<|im_end|>\n<|im_start|>assistant\n
Result
<|im_start|>system\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ<|im_end|>\n<|im_start|>assistant\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ ΰΈ’ΰΈ΄ΰΈΰΈΰΈ΅ΰΈΰΉΰΈΰΈΰΈ£ΰΈ±ΰΈΰΈΰΈ£ΰΈ±ΰΈ ΰΈΰΈΈΰΈΰΈΰΉΰΈΰΈΰΈΰΈ²ΰΈ£ΰΉΰΈ«ΰΉΰΈΰΈ±ΰΈΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈ°ΰΉΰΈ£ΰΈΰΈ£ΰΈ±ΰΈ?<|im_end|>\n<|im_start|>user\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ ΰΈΰΈ·ΰΉΰΈΰΉΰΈΰΉΰΈ‘ΰΈ’ΰΈ²ΰΈ§ΰΉΰΈΰΈ·ΰΈΰΈΰΈ°ΰΉΰΈ£<|im_end|>\n<|im_start|>assistant\nΰΈΰΈ·ΰΉΰΈΰΉΰΈΰΉΰΈ‘ΰΈΰΈΰΈΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈΰΈ·ΰΈ \"ΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ ΰΈΰΈ‘ΰΈ£ΰΈ£ΰΈ±ΰΈΰΈΰΉΰΈΰΈͺΰΈ΄ΰΈΰΈΰΈ£ΰΉ ΰΈ‘ΰΈ«ΰΈ΄ΰΈΰΈΰΈ£ΰΈ²ΰΈ’ΰΈΈΰΈΰΈ’ΰΈ² ΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈ΄ΰΈ₯ΰΈΰΈ ΰΈ ΰΈΰΈΰΈ£ΰΈ±ΰΈΰΈΰΈ£ΰΈ²ΰΈΰΈΰΈ²ΰΈΰΈ΅ΰΈΰΈΉΰΈ£ΰΈ΅ΰΈ£ΰΈ‘ΰΈ’ΰΉ ΰΈΰΈΈΰΈΰΈ‘ΰΈ£ΰΈ²ΰΈΰΈΰΈ΄ΰΉΰΈ§ΰΈ¨ΰΈΰΉΰΈ‘ΰΈ«ΰΈ²ΰΈͺΰΈΰΈ²ΰΈ ΰΈΰΈ‘ΰΈ£ΰΈΰΈ΄ΰΈ‘ΰΈ²ΰΈΰΈΰΈ§ΰΈΰΈ²ΰΈ£ΰΈͺΰΈΰΈ΄ΰΈ ΰΈͺΰΈ±ΰΈΰΈΰΈ°ΰΈΰΈ±ΰΈΰΈΰΈ΄ΰΈ’ΰΈ§ΰΈ΄ΰΈ©ΰΈΰΈΈΰΈΰΈ£ΰΈ£ΰΈ‘ΰΈΰΈ£ΰΈ°ΰΈͺΰΈ΄ΰΈΰΈΰΈ΄ΰΉ\"
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\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈΰΈ·ΰΈΰΈΰΈ°ΰΉΰΈ£<|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": "ΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ"
},
{
"role": "user",
"content": "ΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅"
}
]
}'
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\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈΰΈ£ΰΈΈΰΈΰΉΰΈΰΈΰΈ‘ΰΈ«ΰΈ²ΰΈΰΈΰΈ£ΰΈΰΈ·ΰΈΰΈΰΈ°ΰΉΰΈ£<|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 = "ΰΈΰΈ£ΰΈ°ΰΉΰΈΰΈ¨ΰΉΰΈΰΈ’ΰΈΰΈ·ΰΈΰΈΰΈ°ΰΉΰΈ£"
messages = [
{"role": "system", "content": "ΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ"},
{"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 4to 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\nΰΈΰΈΈΰΈΰΈΰΈ·ΰΈΰΈΰΈΉΰΉΰΈΰΉΰΈ§ΰΈ’ΰΈΰΈΰΈΰΈΰΈ³ΰΈΰΈ²ΰΈ‘ΰΈΰΈ΅ΰΉΰΈΰΈ₯ΰΈ²ΰΈΰΉΰΈ₯ΰΈ°ΰΈΰΈ·ΰΉΰΈΰΈͺΰΈ±ΰΈΰΈ’ΰΉ<|im_end|>\n<|im_start|>user\nΰΈͺΰΈ§ΰΈ±ΰΈͺΰΈΰΈ΅ΰΈΰΈ£ΰΈ±ΰΈ<|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, βWhat is the current temperature in San Francisco?β 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 userβs 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": "ΰΈΰΈΈΰΈΰΈ«ΰΈ ΰΈΉΰΈ‘ΰΈ΄ΰΈΰΈ΅ΰΉ San Francisco ΰΈ§ΰΈ±ΰΈΰΈΰΈ΅ΰΉΰΈ΅ΰΉΰΈ₯ΰΈ°ΰΈΰΈ£ΰΈΈΰΉΰΉΰΈΰΈΰΈ΅ΰΉΰΈΰΈ·ΰΈΰΉΰΈΰΉΰΈ²ΰΉΰΈ£ΰΉ?"}]
# 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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Evaluation results
- Thai Exam(Acc) on ThaiExamπΉπ Thai LLM Leaderboard63.890
- M3Exam(Acc) on M3ExamπΉπ Thai LLM Leaderboard70.390