This 8B language model is developed as an extension of ThaiLLM-8B, with a focus on enhancing instruction-following capabilities and financial knowledge. The model is constructed using mergekit that integrates ThaiLLM-8B with Qwen3-8B and THaLLE, the latter of which was trained on 80 CFA examination sets.

THaLLE-0.2-ThaiLLM-8B-fa has the following features:

Supports switching between thinking and non-thinking modes, similar to Qwen3-8B.
Offers enhanced Thai language understanding from ThaiLLM-8B.
Incorporates the financial knowledge and understanding expected of THaLLE fine-tuning.

Usage

Requirements

Since KBTG-Labs/THaLLE-0.2-ThaiLLM-8B-fa is a fine-tuned of Qwen3-8B you will need to install transformers>=4.51.0.

Running using Transformers

Running the script below generates output based on the given input messages.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID: str = "KBTG-Labs/THaLLE-0.2-ThaiLLM-8B-fa"

def inference(messages: list[dict[str, str]], model, tokenizer) -> str:
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=False, # Switches thinking modes.
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    generated_ids = model.generate(
        model_inputs.input_ids,
        max_new_tokens=768,
        do_sample=False,
        temperature=None,
        top_p=None,
        top_k=None,
        pad_token_id=tokenizer.eos_token_id,
    )
    generated_ids = [
        output_ids[len(input_ids) :]
        for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

if __name__ == "__main__":
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.bfloat16,
        device_map="auto",
    )

    messages = [{"role": "user", "content": "สวัสดี!"}]
    inference(messages, model, tokenizer)

Results

For more details, see our Technical Report.

Model	M3 Exam	M6 Exam	Flare CFA*	IC
Non-Thinking
`Qwen3-8B`	0.660	0.545	0.753	0.640
`ThaiLLM-8B-Instruct`**	0.707	0.623	0.762	0.720
`THaLLE-0.2-ThaiLLM-8B-fa`	0.725	0.572	0.771	0.720
Thinking
`Qwen3-8B`	0.706	0.590	0.806	0.600
`ThaiLLM-8B-Instruct`**	0.720	0.661	0.820	0.720
`THaLLE-0.2-ThaiLLM-8B-fa`	0.779	0.678	0.852	0.840

[*] Flare CFA is "TheFinAI/flare-cfa"

[**] "ThaiLLM-8B-Instruct" is KBTG-Labs/ThaiLLM-8B-Instruct

vLLM was used for evaluations, results might vary.

Citation

If you find our work useful, please cite:

@misc{labs2026thallethaillmdomainspecializedsmallllms,
      title={THaLLE-ThaiLLM: Domain-Specialized Small LLMs for Finance and Thai -- Technical Report}, 
      author={KBTG Labs and : and Anuruth Lertpiya and Danupat Khamnuansin and Kantapong Sucharitpongpan and Pornchanan Balee and Tawunrat Chalothorn and Thadpong Pongthawornkamol and Monchai Lertsutthiwong},
      year={2026},
      eprint={2601.04597},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.04597}, 
}