☁️ SiamGPT-32B: A Robust Spokesperson for Linguistically Stable Thai Generation

🇹🇭 SiamGPT-32B is a fine-tuned variant of Qwen3-32B, optimized specifically for high-fidelity Thai language generation, instruction following, and multi-turn dialogue stability. Developed by SIAM.AI, this model addresses the critical challenge of "multilingual interference" (code-switching) often found in open-weight models when processing Thai.

ℹ️ Model Description

• Model type: A 32B instruct decoder-only model based on Qwen3 architecture.
• Primary Language(s): Thai 🇹🇭 and English 🇬🇧
• Context Length: 8,192 tokens
• License: Apache 2.0 License

🏆 Achieves the highest overall score among open-weights models in the 30B-32B class on the SEA-HELM Thai benchmarks.

🚀 Key Features

• 🗣️ Reduced Code-Switching: Specifically trained to mitigate the injection of Chinese, Hindi, or English tokens into Thai sentences, ensuring output suitable for production user-facing applications.
• 🧠 Agentic Focus: Designed as a final response synthesizer for multi-agent systems, prioritizing strict formatting constraints and reasoning over open-ended creative writing.

🎮 Try the Demo

Experience SiamGPT-32B in action! We use this model as the final responder for our SiamGPT Agentic Tourism platform. See how it handles complex Thai queries and multi-agent synthesis yourself:

🥇 SEA-HELM Leaderboard (Thai)

Full comparison against regional models (Typhoon 2.5 & OpenThaiGPT)

Metric	SiamGPT-32B (Ours)	Typhoon 2.5	OpenThaiGPT R1
Total Overall	63.59	60.44	55.28
Instruction Following	83.00	79.00	54.00
Multi-turn	75.81	76.16	59.69
NLU (Understanding)	67.95	65.56	59.89
NLG (Generation)	42.06	56.70	54.31
NLR (Reasoning)	68.59	55.54	65.38
Safety	44.19	29.68	41.42

📈 Stability & Control vs. Base Model

Full ablation analysis against Qwen3-32B baseline

Metric	Qwen3-32B	SiamGPT-32B	Improvement
Stability (Code Switch)	87.70	90.40	+2.70
Instruction Following (IF-Eval)	75.47	83.00	+7.53
Multi-Turn Dialogue (MT-Bench)	57.94	75.81	+17.87
Thai Exam (Knowledge)	61.40	63.00	+1.60
NLU (Natural Language Understanding)	59.80	67.95	+8.15

💻 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "siamaids/SiamGPT-32B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {"role": "system", "content": "You are SiamGPT, a helpful Thai AI assistant."},
    {"role": "user", "content": "ช่วยแนะนำสถานที่เที่ยวในเชียงใหม่ให้หน่อย"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
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]
print(response)

⚡️ Deploy via vLLM

• vllm >= 0.8.5

pip install vllm
vllm serve siamaids/SiamGPT-32B --max-model-len 8192 --reasoning-parser qwen3 --gpu-memory-utilization 0.95

💥 Deploy via SGLang

• sglang >= 0.4.6post1

pip install sglang
python -m sglang.launch_server --model-path siamaids/SiamGPT-32B --reasoning-parser qwen3

⚠️ Limitations & Risks

• "Translationese" Artifacts: Due to the heavy reliance on translated instruction data, the model may occasionally exhibit phrasing that, while grammatically correct, lacks the stylistic naturalness of native Thai speakers.
• Looping in Creative Writing: The model is optimized for agentic, instruction-following tasks.When used for open-ended creative writing without strong context anchoring, it may exhibit repetition or looping.
• Factuality: Like all LLMs, SiamGPT can hallucinate.It is recommended for use as a response synthesizer in RAG (Retrieval-Augmented Generation) pipelines where ground truth is provided in the context.

📚 Citation

• If you find SiamGPT useful for your work, please cite it using:

@misc{pairatsuppawat2025siamgptqualityfirstfinetuningstable,
      title={SiamGPT: Quality-First Fine-Tuning for Stable Thai Text Generation}, 
      author={Thittipat Pairatsuppawat and Abhibhu Tachaapornchai and Paweekorn Kusolsomboon and Chutikan Chaiwong and Thodsaporn Chay-intr and Kobkrit Viriyayudhakorn and Nongnuch Ketui and Aslan B. Wong},
      year={2025},
      eprint={2512.19455},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.19455}, 
}

🤝 Follow & Support Us

• 🌐 Website: siam.ai
• 💙 Facebook: siam.aicloud
• 📸 Instagram: @siam.ai.cloud
• 🍋 Lemon8: @siam.ai.cloud

🙏 Acknowledgements

We would like to express our deepest gratitude to everyone who uses SiamGPT, provides feedback, or engages with our community. Your voices and insights are invaluable to us.

Every interaction helps us grow and improve as a Thai AI company, driving our mission to build a larger, stronger, and more capable AI ecosystem for Thailand.

Thank you for being part of this journey! 🇹🇭 ✨