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EN
Openthaigpt 1.0.0 13b Chat
OpenThaiGPT 13b 1.0.0 is a 13-billion-parameter Thai chat model based on LLaMA v2, specifically optimized for Thai instructions
Large Language Model Supports Multiple Languages#Thai Large Language Model#Multi-turn Dialogue#Retrieval-Augmented Generation
Downloads 181
Release Time : 4/4/2024
Model Overview
This is an open-source large language model optimized for Thai, supporting both Thai and English, focusing on Q&A and dialogue tasks, with excellent performance in various Thai exams
Model Features
Leading Thai Language Capability
Achieves the highest average scores in multiple Thai exams, surpassing other open-source Thai LLMs
Optimized Vocabulary Design
Integrates over 10,000 common Thai words, improving generation speed by ten times
Large-scale Training Data
Pre-trained on over 65 billion Thai words and fine-tuned with 1 million Thai instruction examples
Long Context Support
Can process input contexts of up to 4096 Thai words
Multi-turn Dialogue Capability
Supports extended multi-turn dialogue interactions
Model Capabilities
Thai Text Generation
English Text Generation
Q&A System
Dialogue System
Retrieval-Augmented Generation (RAG)
Use Cases
Education
Thai Exam Tutoring
Helps students prepare for Thai-related exams
Excellent performance in various Thai exams
Customer Service
Thai Customer Service Bot
Provides Thai customer service support
Capable of handling complex Thai queries
Content Creation
Thai Content Generation
Generates Thai articles, stories, and other content
Produces fluent and natural Thai text
ð ðđð OpenThaiGPT 13b 1.0.0
OpenThaiGPT 13b 1.0.0 is an advanced Thai language chat model based on LLaMA v2, offering high - performance language processing and rapid response capabilities.
ðđð OpenThaiGPT 13b Version 1.0.0 is an advanced 13 - 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.
âĻ Features
- Leading - edge Thai language LLM: Sets 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: Achieves a higher score on Thai exams than OpenAI GPT 3.5, Google Gemini, and Claude 3 Haiku.
- Support for extended conversations: Enables conversations across multiple turns.
- Support for Retrieval Augmented Generation (RAG): Facilitates enriched response generation.
- Tenfold increase in generation speeds: Thanks to the addition of 10,000 frequently used Thai words to the model's dictionary.
- Pretrained on a large Thai language corpus: Pretrained upon a foundation of more than 65 billion Thai language words and meticulously fine - tuned with over 1 million Thai instruction examples.
- Large input context handling: Capable of understanding and processing input contexts of up to 4096 Thai words, allowing for detailed and complex instructions.
ð Documentation
Benchmark by OpenThaiGPT Eval
Please take a look at OTG 13b (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 set, Zero - shot learning. Benchmark source code and exams information: https://github.com/OpenThaiGPT/openthaigpt_eval
(Updated on: 7 April 2024)
Benchmark on M3Exam evaluated by an external party (Float16.cloud)
| Models | ENGLISH (M3EXAM) | THAI (M3EXAM) |
|---|---|---|
| OTG - 7b | 40.92 % | 25.14 % |
| OTG - 13b | 53.69 % | 36.49 % |
| OTG - 70b | 72.58 % | 48.29 % |
| GPT - 3.5 - turbo - 0613* | - | 34.1 % |
| GPT - 4 - 0613* | - | 56.0 % |
More information: https://blog.float16.cloud/the - first - 70b - thai - llm/
ð License
- Source Code: Licensed under the Apache Software License 2.0.
- Weight: Allows for research and commercial uses.
ðĪ Sponsors
ð ïļ Supports
- Official website: https://openthaigpt.aieat.or.th
- Facebook page: https://web.facebook.com/groups/openthaigpt
- Discord server: Join here for discussion and support.
- E - mail: kobkrit@aieat.or.th
ðŧ Usage Examples
Prompt Format
The 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
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 on 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)
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