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--- |
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license: apache-2.0 |
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language: |
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- th |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- openthaigpt |
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- llama |
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--- |
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# 🇹🇭 OpenThaiGPT 7b 1.0.0 |
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<img src="https://1173516064-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FvvbWvIIe82Iv1yHaDBC5%2Fuploads%2Fb8eiMDaqiEQL6ahbAY0h%2Fimage.png?alt=media&token=6fce78fd-2cca-4c0a-9648-bd5518e644ce |
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https://openthaigpt.aieat.or.th/" width="200px"> |
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🇹🇭 OpenThaiGPT 7b Version 1.0.0-beta is a Thai language 7B-parameter LLaMA v2 Chat model finetuned to Thai instructions and extend more than 10,000 most popular Thai words vocabularies into LLM's dictionary for turbo speed. |
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## Features |
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- State-of-the-Art Thai language LLM, Acheive the highest average score over all Thai opensource LLMs on 9 Thai language exams. |
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- Multi-turn Conversation Support |
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- Retrieval Augmented Generation (RAG) Support |
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## Benchmark |
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| **Exams** | **OTG 7b (Aug 2023)** | **OTG 13b (Dec 2023)** | **OTG 7b (March 2024)** | **OTG 13b (March 2024)** | **OTG 70b (March 2024)** | **SeaLLM 7b v1** | **SeaLLM 7b v2** | **TyphoonGPT 7b** | **SeaLion 7b** | **WanchanGLM 7b** | **Sailor-7B-Chat** | **GPT3.5** | **GPT4** | **Gemini Pro** | **Gemini 1.5** | **Claude 3 Haiku** | **Claude 3 Sonnet** | **Claude 3 Opus** | |
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|----------------------------------|-----------------------|------------------------|-------------------------|--------------------------|--------------------------|------------------|------------------|--------------------|----------------|-------------------|--------------------|------------|----------|----------------|----------------|--------------------|---------------------|-------------------| |
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| **A-Level** | 17.50% | 34.17% | 25.00% | 30.83% | 45.83% | 18.33% | 34.17% | N/A | 21.67% | 17.50% | 40.00% | 38.33% | 65.83% | 56.67% | 55.83% | 58.33% | 59.17% | 77.50% | |
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| **TGAT** | 24.00% | 22.00% | 22.00% | 36.00% | 36.00% | 14.00% | 28.00% | N/A | 24.00% | 16.00% | 34.00% | 28.00% | 44.00% | 22.00% | 28.00% | 36.00% | 34.00% | 46.00% | |
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| **TPAT1** | 22.50% | 47.50% | 42.50% | 27.50% | 62.50% | 22.50% | 27.50% | N/A | 22.50% | 17.50% | 40.00% | 45.00% | 52.50% | 52.50% | 50.00% | 52.50% | 50.00% | 62.50% | |
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| **ic_all_test** | 8.00% | 28.00% | 76.00% | 84.00% | 68.00% | 16.00% | 28.00% | N/A | 24.00% | 16.00% | 24.00% | 40.00% | 64.00% | 52.00% | 32.00% | 44.00% | 64.00% | 72.00% | |
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| **facebook_beleble_tha** | 25.00% | 45.00% | 34.50% | 39.50% | 70.00% | 13.50% | 51.00% | N/A | 27.00% | 24.50% | 63.00% | 50.00% | 72.50% | 65.00% | 74.00% | 63.50% | 77.00% | 90.00% | |
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| **xcopa_th_200** | 45.00% | 56.50% | 49.50% | 51.50% | 74.50% | 26.50% | 47.00% | N/A | 51.50% | 48.50% | 68.50% | 64.00% | 82.00% | 68.00% | 74.00% | 64.00% | 80.00% | 86.00% | |
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| **xnli2.0_tha** | 33.50% | 34.50% | 39.50% | 31.00% | 47.00% | 21.00% | 43.00% | N/A | 37.50% | 33.50% | 16.00% | 50.00% | 69.00% | 53.00% | 54.50% | 50.00% | 68.00% | 68.50% | |
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| **ONET M3** | 17.85% | 38.86% | 34.11% | 39.36% | 56.15% | 15.58% | 23.92% | N/A | 21.79% | 19.56% | 21.37% | 37.91% | 49.97% | 55.99% | 57.41% | 52.73% | 40.60% | 63.87% | |
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| **ONET M6** | 21.14% | 28.87% | 22.53% | 23.32% | 42.85% | 15.09% | 19.48% | N/A | 16.96% | 20.67% | 28.64% | 34.44% | 46.29% | 45.53% | 50.23% | 34.79% | 38.49% | 48.56% | |
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|----------------------------------|-----------------------|------------------------|-------------------------|--------------------------|--------------------------|------------------|------------------|--------------------|----------------|-------------------|--------------------|------------|----------|----------------|----------------|--------------------|---------------------|-------------------| |
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| **Average Score** | 23.83% | 37.27% | 38.40% | 40.33% | 55.87% | 18.06% | 33.56% | N/A | 27.44% | 23.75% | 37.28% | 43.07% | 60.68% | 52.30% | 52.89% | 50.65% | 56.81% | 68.32% | |
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## Licenses |
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**Source Code**: License Apache Software License 2.0.<br> |
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**Weight**: Research and **Commercial uses**.<br> |
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## Sponsors |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/42d-GioSs4evIdNuMAaPB.png" width="600px"> |
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## Supports |
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- Official website: https://openthaigpt.aieat.or.th |
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- Facebook page: https://web.facebook.com/groups/openthaigpt |
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- A Discord server for discussion and support [here](https://discord.gg/rUTp6dfVUF) |
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- E-mail: kobkrit@aieat.or.th |
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## Prompt Format |
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Prompt format is based on Llama2 with a small modification (Adding "###" to specify the context part) |
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``` |
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<s>[INST] <<SYS> |
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{system_prompt} |
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<</SYS>> |
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{human_turn1}###{context_turn1} [/INST]{assistant_turn1}</s><s>{human_turn2}###{context_turn2} [/INST] ... |
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``` |
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Practically, when usually used "\n" for a new line so, |
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``` |
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<s>[INST] <<SYS>\n{system_prompt}\n<</SYS>>\n\n{human_turn1}###{context_turn1} [/INST]{assistant_turn1}</s><s>{human_turn2}###{context_turn2} [/INST] ... |
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``` |
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### System prompt: |
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``` |
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You are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด |
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``` |
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### Single Turn Conversation Example |
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``` |
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<s>[INST] <<SYS>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด\n<</SYS>>\n\nสวัสดีครับ [/INST] |
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``` |
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### Single Turn Conversation with Context (RAG) Example |
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``` |
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<s>[INST] <<SYS>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด\n<</SYS>>\n\nกรุงเทพมีพื้นที่เท่าไร่###กรุงเทพมหานคร เป็นเมืองหลวง นครและมหานครที่มีประชากรมากที่สุดของประเทศไทย กรุงเทพมหานครมีพื้นที่ทั้งหมด 1,568.737 ตร.กม. มีประชากรตามทะเบียนราษฎรกว่า 8 ล้านคน [/INST] |
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``` |
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### Multi Turn Conversation Example |
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``` |
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<s>[INST] <<SYS>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด\n<</SYS>>\n\nสวัสดี [/INST] |
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``` |
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## How to use |
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1. install VLLM (https://github.com/vllm-project/vllm) |
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2. python -m vllm.entrypoints.api_server --model /path/to/model --tensor-parallel-size num_gpus |
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3. run inference (CURL example) |
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``` |
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curl --request POST \ |
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--url http://localhost:8000/generate \ |
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--header "Content-Type: application/json" \ |
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--data '{"prompt": "<s>[INST] <<SYS>>\nYou are a question answering assistant. Answer the question as truthful and helpful as possible คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด\n<</SYS>>\n\nอยากลดความอ้วนต้องทำอย่างไร [/INST]","use_beam_search": false, "temperature": 0.1, "max_tokens": 512, "top_p": 0.75, "top_k": 40, "frequency_penalty": 0.3 "stop": "</s>"}' |
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``` |
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### Authors |
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* Kobkrit Viriyayudhakorn (kobkrit@aieat.or.th) |
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* Sumeth Yuenyong (sumeth.yue@mahidol.edu) |
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* Thaweewat Rugsujarit (thaweewr@scg.com) |
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* Jillaphat Jaroenkantasima (autsadang41@gmail.com) |
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* Norapat Buppodom (new@norapat.com) |
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* Koravich Sangkaew (kwankoravich@gmail.com) |
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* Peerawat Rojratchadakorn (peerawat.roj@gmail.com) |
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* Surapon Nonesung (nonesungsurapon@gmail.com) |
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* Chanon Utupon (chanon.utupon@gmail.com) |
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* Sadhis Wongprayoon (sadhis.tae@gmail.com) |
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* Nucharee Thongthungwong (nuchhub@hotmail.com) |
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* Chawakorn Phiantham (mondcha1507@gmail.com) |
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* Patteera Triamamornwooth (patt.patteera@gmail.com) |
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* Nattarika Juntarapaoraya (natt.juntara@gmail.com) |
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* Kriangkrai Saetan (kraitan.ss21@gmail.com) |
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* Pitikorn Khlaisamniang (pitikorn32@gmail.com) |
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<i>Disclaimer: Provided responses are not guaranteed.</i> |
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