scb10x/typhoon-v1.5-72b-instruct-awq

Typhoon-1.5-72B-instruct: Thai Large Language Model (Instruct-AWQ)

Typhoon-1.5-72B-instruct is a instruct Thai 🇹🇭 large language model with 72 billion parameters, and it is based on Qwen1.5-72B.

We also have a newer release of 1.5x 70B, which is better for application use cases. here

For release post, please see our blog.

Model Description

• Model type: A 72B instruct decoder-only model based on Qwen1.5 archtecture.
• Requirement: vllm (https://pypi.org/project/vllm/) 0.3.2 or newer.
• Primary Language(s): Thai 🇹🇭 and English 🇬🇧
• License: Qwen License

Performance

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**

Usage Example

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
quant_path = "scb10x/typhoon-v1.5-72b-instruct-awq"
llm = LLM(model=quant_path, quantization='awq', max_model_len=8192)
tokenizer = AutoTokenizer.from_pretrained(quant_path)

messages = [
    {"role": "user", "content": "ขอสูตรไก่ย่าง"},
]
prompts = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False
)
sampling_params = SamplingParams(repetition_penalty=1.15, top_p=0.6, temperature=0.9, max_tokens=1024, stop=['<|im_end|>', '<|im_start|>'])
outputs = llm.generate(prompts, sampling_params=sampling_params)
print(outputs[0].outputs)

Chat Template

We use chatml chat-template.

{% 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.

Follow us

https://twitter.com/opentyphoon

Support / Ask any question

https://discord.gg/CqyBscMFpg

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