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openthaigpt
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openthaigpt1.5-7b-instruct

Text Generation
Transformers
Safetensors
GGUF
Thai
English
qwen2
openthaigpt
qwen
conversational
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+ ---
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+ license: other
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+ license_name: qwen
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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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+ - qwen
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+ ---
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+
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+ # 🇹🇭 OpenThaiGPT 7b 1.5.0 Chat
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+ ![OpenThaiGPT](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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+ [More Info](https://openthaigpt.aieat.or.th/)
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+
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+ 🇹🇭 **OpenThaiGPT 7b Version 1.5.0** 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.
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+
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+ ## Highlights
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+ - **State-of-the-art Thai language LLM**, achieving the highest average scores across various Thai language exams compared to other open-source Thai LLMs.
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+ - **Multi-turn conversation support** for extended dialogues.
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+ - **Retrieval Augmented Generation (RAG) compatibility** for enhanced response generation.
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+ - **Impressive context handling**: Processes up to 131,072 tokens of input and generates up to 8,192 tokens, enabling detailed and complex interactions.
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+
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+ ## Benchmark on [OpenThaiGPT Eval](https://huggingface.co/datasets/openthaigpt/openthaigpt_eval)
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+ ** Please take a look at ``openthaigpt/openthaigpt1.5-7b-instruct`` for this model's evaluation result.
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+ | **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** |
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+ |:------------------------------:|:---------------------------------------------:|:-------------------------------------:|:-----------------------------:|:----------------------------------:|
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+ | **01_a_level** | 59.17% | 61.67% | 75.00% | 76.67% |
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+ | **02_tgat** | 46.00% | 40.00% | 48.00% | 46.00% |
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+ | **03_tpat1** | 52.50% | 50.00% | 55.00% | 55.00% |
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+ | **04_investment_consult** | 60.00% | 52.00% | 80.00% | 72.00% |
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+ | **05_facebook_beleble_th_200** | 87.50% | 88.00% | 90.00% | 90.00% |
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+ | **06_xcopa_th_200** | 84.50% | 85.50% | 90.00% | 90.50% |
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+ | **07_xnli2.0_th_200** | 62.50% | 63.00% | 65.50% | 70.50% |
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+ | **08_onet_m3_thai** | 76.00% | 56.00% | 76.00% | 84.00% |
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+ | **09_onet_m3_social** | 95.00% | 95.00% | 90.00% | 95.00% |
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+ | **10_onet_m3_math** | 43.75% | 25.00% | 37.50% | 37.50% |
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+ | **11_onet_m3_science** | 53.85% | 61.54% | 65.38% | 73.08% |
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+ | **12_onet_m3_english** | 93.33% | 93.33% | 96.67% | 96.67% |
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+ | **13_onet_m6_thai** | 55.38% | 60.00% | 60.00% | 56.92% |
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+ | **14_onet_m6_math** | 41.18% | 58.82% | 23.53% | 41.18% |
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+ | **15_onet_m6_social** | 67.27% | 76.36% | 63.64% | 65.45% |
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+ | **16_onet_m6_science** | 50.00% | 57.14% | 64.29% | 67.86% |
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+ | **17_onet_m6_english** | 73.08% | 82.69% | 86.54% | 90.38% |
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+ | **Micro Average** | 69.97% | 71.09% | 75.02% | <b style="color:blue">76.73%</b> |
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+
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+
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+
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+
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+ 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
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+
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+ (Updated on: 30 September 2024)
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+
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+ ## Benchmark on [scb10x/thai_exam](https://huggingface.co/datasets/scb10x/thai_exam)
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+
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+ | Models | **Thai Exam (Acc)** |
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+ |:----------------------------------------------------------:|:-------------------:|
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+ | **api/claude-3-5-sonnet-20240620** | 69.2 |
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+ | <b style="color:blue">**openthaigpt/openthaigpt1.5-72b-instruct***</b> | <b style="color:blue">64.07</b> |
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+ | **api/gpt-4o-2024-05-13** | 63.89 |
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+ | **hugging-quants/Meta-Llama-3.1-405B-Instruct-AWQ-INT4** | 63.54 |
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+ | **Qwen/Qwen2-72B-Instruct** | 58.23 |
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+ | **meta-llama/Meta-Llama-3.1-70B-Instruct** | 58.23 |
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+ | **scb10x/llama-3-typhoon-v1.5x-70b-instruct** | 58.76 |
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+ | **Qwen/Qwen2.5-14B-Instruct** | 57.35 |
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+ | **api/gpt-4o-mini-2024-07-18** | 54.51 |
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+ | <b style="color:blue">**openthaigpt/openthaigpt1.5-7b-instruct***</b> | <b style="color:blue">52.04</b> |
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+ | **SeaLLMs/SeaLLMs-v3-7B-Chat** | 51.33 |
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+ | **openthaigpt/openthaigpt-1.0.0-70b-chat** | 50.09 |
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+ \* Evaluated by OpenThaiGPT team using SCBx's Thai Exam
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+
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+ ## Licenses
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+ * Built with Qwen
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+ * Qwen License: Allow **Research** and
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+ **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.<br>
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+
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+ ## Sponsors
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/5fcd9c426d942eaf4d1ebd30/3kjN6kuTzXDXQ6o1RFvHX.png" width="600px">
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+
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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: [email protected]
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+
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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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+ <|im_start|>system\n{sytem_prompt}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n
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+ ```
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+
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+ ### System prompt:
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+ ```
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+ คุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์
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+ ```
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+
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+ ### Examples
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+
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+ #### Single Turn Conversation Example
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+ ```
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+ <|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n
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+ ```
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+
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+ #### Single Turn Conversation with Context (RAG) Example
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+ ```
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+ <|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร เป็นเมืองหลวง นครและมหานครที่มีประชากรมากที่สุดของประเทศไทย กรุงเทพมหานครมีพื้นที่ทั้งหมด 1,568.737 ตร.กม. มีประชากรตามทะเบียนราษฎรกว่า 8 ล้านคน\nกรุงเทพมหานครมีพื้นที่เท่าไร่<|im_end|>\n<|im_start|>assistant\n
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+ ```
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+
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+ #### Multi Turn Conversation Example
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+
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+ ##### First turn
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+ ```
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+ <|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n
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+ ```
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+
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+ ##### Second turn
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+ ```
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+ <|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\nสวัสดีครับ ยินดีต้อนรับครับ คุณต้องการให้ฉันช่วยอะไรครับ?<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร ชื่อเต็มยาวๆคืออะไร<|im_end|>\n<|im_start|>assistant\n
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+ ```
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+
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+ ชื่อเต็มของกรุงเทพมหานครคือ \"กรุงเทพมหานคร อมรรัตนโกสินทร์ มหินทรายุธยา มหาดิลกภพ นพรัตนราชธานีบูรีรมย์ อุดมราชนิเวศน์มหาสถาน อมรพิมานอวตารสถิต สักกะทัตติยวิษณุกรรมประสิทธิ์\"
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+
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+ ##### Result
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+ ```
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+ <|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\nสวัสดีครับ ยินดีต้อนรับครับ คุณต้องการให้ฉันช่วยอะไรครับ?<|im_end|>\n<|im_start|>user\nกรุงเทพมหานคร ชื่อเต็มยาวๆคืออะไร<|im_end|>\n<|im_start|>assistant\nชื่อเต็มของกรุงเทพมหานครคือ \"กรุงเทพมหานคร อมรรัตนโกสินทร์ มหินทรายุธยา มหาดิลกภพ นพรัตนราชธานีบูรีรมย์ อุดมราชนิเวศน์มหาสถาน อมรพิมานอวตารสถิต สักกะทัตติยวิษณุกรรมประสิทธิ์\"
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+ ```
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+
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+ ## How to use
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+
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+ ### Huggingface
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "openthaigpt/openthaigpt1.5-72b-instruct"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype="auto",
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+ device_map="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ prompt = "ประเทศไทยคืออะไร"
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+ messages = [
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+ {"role": "system", "content": "คุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์"},
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+ {"role": "user", "content": prompt}
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+ ]
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+ text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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+
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+ generated_ids = model.generate(
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+ **model_inputs,
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+ max_new_tokens=512
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+ )
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+ generated_ids = [
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+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+ ]
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+
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+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ ```
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+
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+ ### vLLM
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+
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+ 1. Install VLLM (https://github.com/vllm-project/vllm)
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+
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+ 2. Run server
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+ ```bash
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+ vllm serve openthaigpt/openthaigpt1.5-72b-instruct --tensor-parallel-size 4
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+ ```
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+ 3. Run inference (CURL example)
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+ ```bash
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+ curl -X POST 'http://127.0.0.1:8000/v1/completions' \
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+ -H 'Content-Type: application/json' \
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+ -d '{
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+ "model": ".",
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+ "prompt": "<|im_start|>system\nคุณคือผู้ช่วยตอบคำถามที่ฉลาดและซื่อสัตย์<|im_end|>\n<|im_start|>user\nสวัสดีครับ<|im_end|>\n<|im_start|>assistant\n",
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+ "max_tokens": 512,
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+ "temperature": 0.7,
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+ "top_p": 0.8,
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+ "top_k": 40,
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+ "stop": ["<|im_end|>"]
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+ }'
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+ ```
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+
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+ ### Processing Long Texts
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+ The current `config.json` is set for context length up to 32,768 tokens.
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+ 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.
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+
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+ For supported frameworks, you could add the following to `config.json` to enable YaRN:
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+ ```json
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+ {
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+ ...
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+ "rope_scaling": {
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+ "factor": 4.0,
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+ "original_max_position_embeddings": 32768,
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+ "type": "yarn"
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+ }
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+ }
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+ ```
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+
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+ ### GPU Memory Requirements
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+ | **Number of Parameters** | **FP 16 bits** | **8 bits (Quantized)** | **4 bits (Quantized)** | **Example Graphic Card for 4 bits** |
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+ |------------------|----------------|------------------------|------------------------|---------------------------------------------|
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+ | **7b** | 24 GB | 12 GB | 6 GB | Nvidia RTX 4060 8GB |
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+ | **13b** | 48 GB | 24 GB | 12 GB | Nvidia RTX 4070 16GB |
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+ | **72b** | 192 GB | 96 GB | 48 GB | Nvidia RTX 4090 24GB x 2 cards |
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+
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+ ### Authors
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+ * Sumeth Yuenyong ([email protected])
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+ * Kobkrit Viriyayudhakorn ([email protected])
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+ * Apivadee Piyatumrong ([email protected])
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+ * Jillaphat Jaroenkantasima ([email protected])
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+ * Thaweewat Rugsujarit ([email protected])
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+ * Norapat Buppodom ([email protected])
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+ * Koravich Sangkaew ([email protected])
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+ * Peerawat Rojratchadakorn ([email protected])
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+ * Surapon Nonesung ([email protected])
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+ * Chanon Utupon ([email protected])
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+ * Sadhis Wongprayoon ([email protected])
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+ * Nucharee Thongthungwong ([email protected])
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+ * Chawakorn Phiantham ([email protected])
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+ * Patteera Triamamornwooth ([email protected])
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+ * Nattarika Juntarapaoraya ([email protected])
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+ * Kriangkrai Saetan ([email protected])
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+ * Pitikorn Khlaisamniang ([email protected])
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+
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+ <i>Disclaimer: Provided responses are not guaranteed.</i>