Typhoon T1 3B (Research Preview)

Typhoon T1 3B (Research Preview) is the first in a new family of open reasoning model "Typhoon T". Reasoning model is a novel type of model that think longer before giving a final answer.

Typhoon T1 3B (Research Preview) is built on top of Typhoon 2 3B Instruct. It has improved performance on challenging benchmarks like GPQA, MMLU Pro, and AI Mathematics Olympiad validation set.

[!NOTE] 🚀 2025-02-01 Update: We have released a new version of Typhoon T1 3B (Research Preview) with the ability to 🇹🇭 generate Thai reasoning traces, improved Thai performance in general, and enhanced instruction following. This version has the a comparative level of English performance to v2025-01-23.

Key Points

• Typhoon T1 is a new family of open reasoning models developed by SCB 10X
• Typhoon T1 3B (Research Preview), the first in the Typhoon T family, shows improved performance across challenging benchmarks compared to the original Typhoon 2 3B Instruct
• Typhoon T1 3B (Research Preview) offers a fast, low-compute requirements model, yet is capable in a variety of tasks by scaling test-time compute, enabling the model to think longer before giving a final answer. Typhoon T1 3B (Research Preview) is able to reason across domains, unlike many open reasoning models limited to mathematics and coding
• We open our recipe for data pipeline and training this model without distilling from other reasoning models
• We introduce a new thinking paradigm for reasoning models, structured thinking, where we add auxiliary tokens to help structure the thinking process of the model. This approach shows an increase in performance over a common variant of separating only thought and response parts based on our experiments
• Typhoon T1 3B (Research Preview) v2025-02-01 is the first reasoning model where we intentionally equipped the model with the ability to generate Thai reasoning traces, improving transparency and interpretability of the model.

For more technical details, please visit our technical blog.

• To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include llama-3.2 in the model name.

Performance

MMLU Pro (↑), 5-shot

Model description

• Model type: A 3B instruct decoder-only model based on Llama architecture.
• Requirement: transformers 4.46.1 or newer.
• Primary Language(s): English 🇬🇧 and Thai 🇹🇭
• License: Llama 3.2 Community License

Usage Examples

⚠️ Please note that max_new_tokens should be at least 512, but is recommended at a minimum of 1,024 to provide space for complete generation.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "scb10x/llama-3.2-typhoon-t1-3b-research-preview"
revision = "main" # To use the previous version comment this line
# revision = "v2025-01-23" # To use the previous version uncomment this line

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

messages = [
    {"role": "user", "content": "หากแปลคำว่า \"ไต้ฝุ่น\" เป็นภาษาอังกฤษ ในคำที่ถูกแปลแล้วจะมีตัวอักษร \"o\" ทั้งหมดกี่ตัว"},
]

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=1024,
    eos_token_id=terminators,
    do_sample=False,
    temperature=0.0,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

OpenAI API-compatible Server with vLLM

pip install vllm
vllm serve scb10x/llama-3.2-typhoon-t1-3b-research-preview

# To serve the previous version, add the revision parameter as shown below
# vllm serve scb10x/llama-3.2-typhoon-t1-3b-research-preview --revision v2025-01-23
# see more information at https://docs.vllm.ai/

Intended uses & limitations

While we made an effort to make our model safe, like all generative models, it may generate unsafe content in rare cases. Introducing a reasoning model paradigm may introduce some unforeseen behaviors, as model safety in the reasoning domain is a relatively new and ongoing area of research.

Follow Us

https://twitter.com/opentyphoon

Support

https://discord.gg/us5gAYmrxw

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 6
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 6
• total_train_batch_size: 288
• total_eval_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 2.0

Citation

@misc{taveekitworachai2025typhoont1openthai,
      title={Typhoon T1: An Open Thai Reasoning Model}, 
      author={Pittawat Taveekitworachai and Potsawee Manakul and Kasima Tharnpipitchai and Kunat Pipatanakul},
      year={2025},
      eprint={2502.09042},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.09042}, 
}