---
license: other
license_name: qwen
language:
- th
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- openthaigpt
- qwen
model-index:
- name: OpenThaiGPT1.5-7b
results:
- task:
type: text-generation
dataset:
name: ThaiExam
type: multiple_choices
metrics:
- name: Thai Exam(Acc)
type: accuracy
value: 52.04
source:
name: ðđð 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: 54.01
source:
name: ðđð Thai LLM Leaderboard
url: https://huggingface.co/spaces/ThaiLLM-Leaderboard/leaderboard
---
# ðđð OpenThaiGPT 7b 1.5 Instruct
[More Info](https://openthaigpt.aieat.or.th/)
ðđð **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](https://huggingface.co/datasets/openthaigpt/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](https://huggingface.co/datasets/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](https://huggingface.co/datasets/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](https://discord.gg/rUTp6dfVUF)
- 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:
```
āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ
```
### Examples
#### Single Turn Conversation Example
```
<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ<|im_end|>\n<|im_start|>assistant\n
```
#### Single Turn Conversation with Context (RAG) Example
```
<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āđāļāđāļāđāļĄāļ·āļāļāļŦāļĨāļ§āļ āļāļāļĢāđāļĨāļ°āļĄāļŦāļēāļāļāļĢāļāļĩāđāļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļĄāļēāļāļāļĩāđāļŠāļļāļāļāļāļāļāļĢāļ°āđāļāļĻāđāļāļĒ āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļĄāļĩāļāļ·āđāļāļāļĩāđāļāļąāđāļāļŦāļĄāļ 1,568.737 āļāļĢ.āļāļĄ. āļĄāļĩāļāļĢāļ°āļāļēāļāļĢāļāļēāļĄāļāļ°āđāļāļĩāļĒāļāļĢāļēāļĐāļāļĢāļāļ§āđāļē 8 āļĨāđāļēāļāļāļ\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļĄāļĩāļāļ·āđāļāļāļĩāđāđāļāđāļēāđāļĢāđ<|im_end|>\n<|im_start|>assistant\n
```
#### Multi Turn Conversation Example
##### First turn
```
<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ<|im_end|>\n<|im_start|>assistant\n
```
##### Second turn
```
<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ<|im_end|>\n<|im_start|>assistant\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ āļĒāļīāļāļāļĩāļāđāļāļāļĢāļąāļāļāļĢāļąāļ āļāļļāļāļāđāļāļāļāļēāļĢāđāļŦāđāļāļąāļāļāđāļ§āļĒāļāļ°āđāļĢāļāļĢāļąāļ?<|im_end|>\n<|im_start|>user\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āļāļ·āđāļāđāļāđāļĄāļĒāļēāļ§āđāļāļ·āļāļāļ°āđāļĢ<|im_end|>\n<|im_start|>assistant\n
```
##### Result
```
<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ<|im_end|>\n<|im_start|>assistant\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ āļĒāļīāļāļāļĩāļāđāļāļāļĢāļąāļāļāļĢāļąāļ āļāļļāļāļāđāļāļāļāļēāļĢāđāļŦāđāļāļąāļāļāđāļ§āļĒāļāļ°āđāļĢāļāļĢāļąāļ?<|im_end|>\n<|im_start|>user\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āļāļ·āđāļāđāļāđāļĄāļĒāļēāļ§āđāļāļ·āļāļāļ°āđāļĢ<|im_end|>\n<|im_start|>assistant\nāļāļ·āđāļāđāļāđāļĄāļāļāļāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļāļ·āļ \"āļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢ āļāļĄāļĢāļĢāļąāļāļāđāļāļŠāļīāļāļāļĢāđ āļĄāļŦāļīāļāļāļĢāļēāļĒāļļāļāļĒāļē āļĄāļŦāļēāļāļīāļĨāļāļ āļ āļāļāļĢāļąāļāļāļĢāļēāļāļāļēāļāļĩāļāļđāļĢāļĩāļĢāļĄāļĒāđ āļāļļāļāļĄāļĢāļēāļāļāļīāđāļ§āļĻāļāđāļĄāļŦāļēāļŠāļāļēāļ āļāļĄāļĢāļāļīāļĄāļēāļāļāļ§āļāļēāļĢāļŠāļāļīāļ āļŠāļąāļāļāļ°āļāļąāļāļāļīāļĒāļ§āļīāļĐāļāļļāļāļĢāļĢāļĄāļāļĢāļ°āļŠāļīāļāļāļīāđ\"
```
## How to use
### Free API Service (hosted by Siam.Ai and Float16.cloud)
#### Siam.AI
```bash
curl https://api.aieat.or.th/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer dummy" \
-d '{
"model": ".",
"prompt": "<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļāļ·āļāļāļ°āđāļĢ<|im_end|>\n<|im_start|>assistant\n",
"max_tokens": 512,
"temperature": 0.7,
"top_p": 0.8,
"top_k": 40,
"stop": ["<|im_end|>"]
}'
```
#### Float16
```bash
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": "āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ"
},
{
"role": "user",
"content": "āļŠāļ§āļąāļŠāļāļĩ"
}
]
}'
```
### OpenAI Client Library (Hosted by VLLM, please see below.)
```python
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\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļāļĢāļļāļāđāļāļāļĄāļŦāļēāļāļāļĢāļāļ·āļāļāļ°āđāļĢ<|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
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "openthaigpt/openthaigpt1.5-7b-instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "āļāļĢāļ°āđāļāļĻāđāļāļĒāļāļ·āļāļāļ°āđāļĢ"
messages = [
{"role": "system", "content": "āļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ"},
{"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
1. Install VLLM (https://github.com/vllm-project/vllm)
2. Run server
```bash
vllm serve openthaigpt/openthaigpt1.5-7b-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.,
```bash
vllm serve openthaigpt/openthaigpt1.5-7b-instruct --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser hermes
```
3. Run inference (CURL example)
```bash
curl -X POST 'http://127.0.0.1:8000/v1/completions' \
-H 'Content-Type: application/json' \
-d '{
"model": ".",
"prompt": "<|im_start|>system\nāļāļļāļāļāļ·āļāļāļđāđāļāđāļ§āļĒāļāļāļāļāļģāļāļēāļĄāļāļĩāđāļāļĨāļēāļāđāļĨāļ°āļāļ·āđāļāļŠāļąāļāļĒāđ<|im_end|>\n<|im_start|>user\nāļŠāļ§āļąāļŠāļāļĩāļāļĢāļąāļ<|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](https://arxiv.org/abs/2309.00071), 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:
```json
{
...
"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, âWhat is the current temperature in San Francisco?â 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 userâs own system.
#### Example:
```python
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": "āļāļļāļāļŦāļ āļđāļĄāļīāļāļĩāđ San Francisco āļ§āļąāļāļāļĩāđāļĩāđāļĨāļ°āļāļĢāļļāđāđāļāļāļĩāđāļāļ·āļāđāļāđāļēāđāļĢāđ?"}]
# 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.