scb10x/llama-3-typhoon-v1.5x-8b-instruct

Llama-3-Typhoon-1.5X-8B-instruct: Thai Large Language Model (Instruct)

Llama-3-Typhoon-1.5X-8B-instruct is an 8 billion parameter instruct model designed for Thai 🇹🇭 language. It demonstrates competitive performance with GPT-3.5-turbo, and is optimized for application use cases, Retrieval-Augmented Generation (RAG), constrained generation, and reasoning tasks.

Built on Typhoon 1.5 8B and Llama 3 8B Instruct. This model is a result of our experiment on cross-lingual transfer. It utilizes the task-arithmetic model editing technique, combining the Thai understanding capability of Typhoon with the human alignment performance of Llama 3 Instruct.

Remark: To acknowledge Meta's efforts in creating the foundation model and comply with the license, we explicitly include "llama-3" in the model name.

Model Description

• Model type: An 8B instruct decoder-only model based on the Llama architecture.
• Requirement: Transformers 4.38.0 or newer.
• Primary Language(s): Thai 🇹🇭 and English 🇬🇧
• License: Llama 3 Community License

Performance

We evaluated the model's performance in Language & Knowledge Capabilities and Instruction Following Capabilities.

• Language & Knowledge Capabilities:
  • Assessed using multiple-choice question-answering datasets such as ThaiExam and MMLU.
• Instruction Following Capabilities:
  • Evaluated based on our beta users' feedback, focusing on two factors:
    • Human Alignment & Reasoning: Ability to generate responses that are clear and logically structured across multiple steps.
      • Evaluated using MT-Bench — How LLMs can answer embedded knowledge to align with human needs.
    • Instruction-following: Ability to adhere to specified constraints in the instruction
      • Evaluated using IFEval — How LLMs can follow specified constraints, such as formatting and brevity.

Remark: We developed the TH pair by translating the original datasets into Thai and conducting a human verification on them.

ThaiExam

Model	ONET	IC	TGAT	TPAT-1	A-Level	Average (ThaiExam)	MMLU
Typhoon-1.5 8B	0.446	0.431	0.722	0.526	0.407	0.5028	0.6136
Typhoon-1.5X 8B	0.478	0.379	0.722	0.5	0.435	0.5028	0.6369
gpt-3.5-turbo-0125	0.358	0.279	0.678	0.345	0.318	0.3956	0.700**

** We report the MMLU score that is reported in GPT-4 Tech Report.

MT-Bench

Model	MT-Bench Thai	MT-Bench English
Typhoon-1.5 8B	6.402	7.275
Typhoon-1.5X 8B	6.902	7.9
gpt-3.5-turbo-0125	6.186	8.181

IFEval

Model	IFEval Thai	IFEval English
Typhoon-1.5 8B	0.548	0.676
Typhoon-1.5X 8B	0.548	0.691
gpt-3.5-turbo-0125	0.479	0.659

Insight

We utilized model editing techniques and found that the most critical feature for generating accurate Thai answers is located in the backend (the upper layers of the transformer block). Accordingly, we incorporated a high ratio of Typhoon components in these backend layers to enhance our model’s performance.

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "scb10x/llama-3-typhoon-v1.5x-8b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [...] # add message here

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=0.95,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Chat Template

We use the Llama 3 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 %}

Intended Uses & Limitations

This model is experimental and might not be fully evaluated for all use cases. Developers should assess risks in the context of their specific applications.

Follow us

https://twitter.com/opentyphoon

Support

https://discord.gg/CqyBscMFpg

SCB 10X Typhoon Team

• Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Natapong Nitarach, Pathomporn Chokchainant, Kasima Tharnpipitchai
• If you find Typhoon-1.5X 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