Model	ONET	IC	TGAT	TPAT-1	A-Level	Average (ThaiExam)	M3Exam	MMLU
Typhoon-1.5 72B	0.562	0.716	0.778	0.5	0.528	0.6168	0.587	0.7271
OpenThaiGPT 1.0.0 70B	0.447	0.492	0.778	0.5	0.319	0.5072	0.493	0.6167
GPT-3.5-turbo(01-2024)	0.358	0.279	0.678	0.345	0.318	0.3956	0.316	0.700**
GPT-4(04-2024)	0.589	0.594	0.756	0.517	0.616	0.6144	0.626	0.864**
** We report the MMLU score that is reported in GPT-4 Tech Report.

Usage Example

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "scb10x/typhoon-v1.5-72b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
) # We do not recommend loading the model using 4-bit and 8-bit BNB as it may produce inaccurate results.

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "ขอสูตรไก่ย่าง"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty=1.15
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Chat Template

We use chatml chat-template.

python

{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n'}}{% endif %}{% endfor %}
{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}

Intended Uses & Limitations

This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.

https://twitter.com/opentyphoon

Support / Ask any question

https://discord.gg/us5gAYmrxw

SCB10X AI Team

Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
If you find Typhoon v1.5 useful for your work, please cite it using:

@article{pipatanakul2023typhoon,
    title={Typhoon: Thai Large Language Models}, 
    author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
    year={2023},
    journal={arXiv preprint arXiv:2312.13951},
    url={https://arxiv.org/abs/2312.13951}
}

Contact Us

General & Collaboration: kasima@scb10x.com, pathomporn@scb10x.com
Technical: kunat@scb10x.com

typhoon-ai/typhoon-v1.5-72b-instruct

Model Description

Performance