🚀 Llama-3-Typhoon-1.5-8B: Thai Large Language Model (Instruct)
Llama-3-Typhoon-1.5-8B-instruct is an instruct Thai 🇹🇭 large language model with 8 billion parameters, based on Llama3-8B. It aims to provide high - quality text generation capabilities in both Thai and English.

For the 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.
✨ Features
- Model type: A 8B instruct decoder - only model based on Llama architecture.
- Requirement: transformers 4.38.0 or newer.
- Primary Language(s): Thai 🇹🇭 and English 🇬🇧
- License: Llama 3 Community License
📊 Performance
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 |
💻 Usage Examples
Basic Usage
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))
Advanced Usage
This example shows the chat template used in the model.
{% 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 %}
📚 Documentation
Chat Template
We use llama3 chat - template.
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
https://discord.gg/us5gAYmrxw
SCB10X AI Team
- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
- If you find Typhoon-8B 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}
}
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