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openthaigpt-1.0.0-7b-chat

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metadata
license: apache-2.0
language:
  - th
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - openthaigpt
  - llama

🇹🇭 OpenThaiGPT 7b 1.0.0

🇹🇭 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.

Features

  • State-of-the-Art Thai language LLM, Acheive the highest average score over all Thai opensource LLMs on 9 Thai language exams.
  • Multi-turn Conversation Support
  • Retrieval Augmented Generation (RAG) Support

Benchmark

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
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%
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%
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%
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%
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%
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%
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%
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%
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%
---------------------------------- ----------------------- ------------------------ ------------------------- -------------------------- -------------------------- ------------------ ------------------ -------------------- ---------------- ------------------- -------------------- ------------ ---------- ---------------- ---------------- -------------------- --------------------- -------------------
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%

Licenses

Source Code: License Apache Software License 2.0.
Weight: Research and Commercial uses.

Sponsors

Supports

Prompt Format

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] ...

Practically, when usually used "\n" for a new line so,

<s>[INST] <<SYS>\n{system_prompt}\n<</SYS>>\n\n{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 คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด

Single Turn Conversation Example 

<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]

Single Turn Conversation with Context (RAG) Example 

<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]

Multi Turn Conversation Example 

<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]

How to use

  1. install VLLM (https://github.com/vllm-project/vllm)
  2. python -m vllm.entrypoints.api_server --model /path/to/model --tensor-parallel-size num_gpus
  3. run inference (CURL example)
curl --request POST \
    --url http://localhost:8000/generate \
    --header "Content-Type: application/json" \
    --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>"}'

Authors

Disclaimer: Provided responses are not guaranteed.