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typhoon-ai/llama-3-typhoon-v1.5-8b-instruct
llama-3-typhoon-v1.5-8b-instruct is a text generation model from typhoon-ai. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.
Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct)
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From the Hugging Face model README
Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct)
Llama-3-Typhoon-1.5-8B-instruct is a instruct Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B.

For release post, please see our blog. *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name.
| Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam | MMLU |
|---|---|---|---|---|---|---|---|---|
| Typhoon-1.0 (Mistral) | 0.379 | 0.393 | 0.700 | 0.414 | 0.324 | 0.442 | 0.391 | 0.547 |
| Typhoon-1.5 8B (Llama3) | 0.446 | 0.431 | 0.722 | 0.526 | 0.407 | 0.506 | 0.460 | 0.614 |
| Sailor 7B | 0.372 | 0.379 | 0.678 | 0.405 | 0.396 | 0.446 | 0.411 | 0.553 |
| SeaLLM 2.0 7B | 0.327 | 0.311 | 0.656 | 0.414 | 0.321 | 0.406 | 0.354 | 0.579 |
| OpenThaiGPT 1.0.0 7B | 0.238 | 0.249 | 0.444 | 0.319 | 0.289 | 0.308 | 0.268 | 0.369 |
| SambaLingo-Thai-Chat 7B | 0.251 | 0.241 | 0.522 | 0.302 | 0.262 | 0.316 | 0.309 | 0.388 |
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "scb10x/llama-3-typhoon-v1.5-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful assistant who're always speak Thai."},
{"role": "user", "content": "ขอสูตรไก่ย่าง"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.4,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
We use llama3 chat-template.
{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}
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
@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}
}