ThaiLLM-8B info

This model is a continued pre-training from Qwen3-8-Base, which underwent training on a diverse corpus of approximately 63 billion tokens.

Important Note: This is a base model that requires instruction fine-tuning to align with specific user requirements and use cases.

For example, the following models have been instruction fine-tuned based on ThaiLLM-8B:

• Typhoon by SCB10X: https://huggingface.co/typhoon-ai/typhoon-s-thaillm-8b-instruct-research-preview

• THaLLE by KBTG: https://huggingface.co/KBTG-Labs/THaLLE-0.2-ThaiLLM-8B-fa

• OpenThaiGPT by AIEAT: https://huggingface.co/openthaigpt/openthaigpt-thaillm-8b-instruct-v0.7.2-research-preview/

• Pathumma by NECTEC: https://huggingface.co/nectec/Pathumma-ThaiLLM-qwen3-8b-it-2.0.0

Data

The training corpus consists of the following datasets:

CuratedData Breakdown

*Token counts calculated using Qwen3 Tokenizer

Requirements

The code of Qwen3 has been integrated into the latest Hugging Face transformers library. We strongly recommend using the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3'

Usage Training

Important: This is a base model and requires instruction fine-tuning before use to ensure optimal performance for your specific tasks and requirements.

Recommended Training Setup

We recommend using LLaMA-Factory for instruction fine-tuning. This framework provides an easy-to-use interface for training language models with various optimization techniques.

Quick Start with LLaMA-Factory

# Clone the repository
git clone https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory

# Install dependencies
pip install -e .

# Example training command for LoRA
llamafactory-cli train \
    --model_name_or_path ThaiLLM/ThaiLLM-8B \
    --stage sft \
    --do_train \
    --finetuning_type lora \
    --dataset your_dataset \
    --template qwen3 \
    --cutoff_len 8192 \
    --learning_rate 5e-05 \
    --num_train_epochs 3.0 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 8 \
    --lr_scheduler_type cosine \
    --max_grad_norm 1.0 \
    --logging_steps 5 \
    --save_steps 100 \
    --warmup_steps 0 \
    --output_dir saves/ThaiLLM-8B-lora \
    --bf16

Usage Inference

Below are code snippets to get quickly started with running the model. First, install the necessary libraries.

pip install -U transformers torch accelerate

from transformers import AutoTokenizer, AutoModelForCausalLM, 
import torch

model_id = "ThaiLLM/ThaiLLM-8B"

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

# Example prompt
prompt = "น้ำบริสุทธิ์มีค่า pH เท่าใด"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response
with torch.inference_mode(): 
    generate_ids = model.generate( 
        inputs.input_ids, 
        max_new_tokens=500, 
        repetition_penalty=1.2, 
        num_beams=1, 
        do_sample=True, 
        top_k=40, 
        top_p=0.75, 
        temperature=0.4, 
        pad_token_id=tokenizer.eos_token_id, 
    )

response = tokenizer.batch_decode(
    generate_ids, 
    skip_special_tokens=True, 
    clean_up_tokenization_spaces=True
)[0]

print(response)

Benchmarks

We evaluated ThaiLLM-8B against Qwen3-8B-Base using multiple-choice question datasets in both Thai and English.
Each benchmark measures the probability of selecting the correct choice based on the model’s next-token prediction.

1. Natural Language Understanding (NLU)

---

2. Average Performance

Highlights:

• ThaiLLM-8B shows large improvements in ThaiExam, Belebele-Thai, and MMLU-TH.
• Gains are especially strong in A-Level (+0.36) and TGAT (+0.29).
• Some slight regressions are seen in MMLU-ENG and Math in M6Exam.

Limitations

• This is a base model and requires instruction fine-tuning for optimal performance
• Performance on specialized domains may require domain-specific fine-tuning
• As with all language models, outputs should be verified for accuracy in critical applications

Citation

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

Dataset Contributor