ThaiLLM-30B-IQ (Medical) is a Thai-language large language model specialized for medical information query and citation-grounded question answering.

The model is fine-tuned from ThaiLLM-30B using supervised fine-tuning (SFT) with medical-domain data. It is designed for retrieval-augmented generation (RAG) workflows, where answers must be generated only from provided medical contexts and returned with explicit citations.

This model does not include a retriever and must be used with externally supplied medical documents.

Intended Use

Suitable for

Medical RAG systems (Thai)
Medical document question answering
Citation-grounded medical QA
Medical education and evaluation

Not suitable for

Medical diagnosis or treatment decisions
Patient-facing clinical advice
Emergency or critical-care use

Model Details

Base model: ThaiLLM-30B
Domain: Medical
Model type: Decoder-only causal language model
Fine-tuning: Supervised fine-tuning (LoRA)
Language: Thai
Precision: bfloat16

Performance (Medical Information Query Setting)

Model	Response (BLEU)	Citations (Jaccard)
ThaiLLM-30B	0.406	0.1786
ThaiLLM-8B-SFT-IQ (Medical)	0.4331	0.5458

Training

Framework: LLaMA-Factory
Method: LoRA-based supervised fine-tuning

Hyperparameter	Value
Learning rate	2e-4
LoRA rank	16
LoRA alpha	16
Sequence length	4096
Epochs	3
Batch size	4

Training data consists of Thai medical question–answer pairs with context grounding and citation supervision.

Training Script
bash
1llamafactory-cli train \ 2 --stage sft \ 3 --do_train True \ 4 --model_name_or_path ${MODEL_PATH} \ 5 --preprocessing_num_workers 16 \ 6 --deepspeed ./examples/deepspeed/ds_z2_config.json \ 7 --finetuning_type lora \ 8 --template qwen3 \ 9 --flash_attn auto \ 10 --dataset thaillm-SFT \ 11 --cutoff_len 4096 \ 12 --learning_rate 2e-4 \ 13 --num_train_epochs 3.0 \ 14 --max_samples 100000 \ 15 --per_device_train_batch_size 1 \ 16 --gradient_accumulation_steps 4 \ 17 --gradient_checkpointing True \ 18 --gradient_checkpointing_kwargs '{"use_reentrant":false}' \ 19 --lr_scheduler_type cosine \ 20 --max_grad_norm 1.0 \ 21 --logging_steps 5 \ 22 --save_steps 100 \ 23 --warmup_steps 0 \ 24 --packing False \ 25 --enable_thinking True \ 26 --report_to tensorboard \ 27 --quantization_bit 4 \ 28 --output_dir "./checkpoints/ThaiLLM-30B-SFT-IQ" \ 29 --overwrite_output_dir True \ 30 --bf16 True \ 31 --plot_loss True \ 32 --trust_remote_code True \ 33 --include_num_input_tokens_seen True \ 34 --optim adamw_8bit \ 35 --lora_rank 16 \ 36 --lora_alpha 16 \ 37 --upcast_layernorm True \ 38 --ddp_timeout 180000000 \ 39 --lora_dropout 0 \ 40 --lora_target all \ 41 --freeze_vision_tower True \ 42 --freeze_multi_modal_projector True \ 43 --use_unsloth False \ 44 --seed 1234

Usage

The model is optimized for strict JSON output and context-only medical answers.

Expected Instruction Format

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ThaiLLM-30B-SFT-IQ"

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

system_prompt = """\
Answer in JSON format with citations only.
Use only the provided medical contexts to answer the question.
Include the fact IDs that support your answer in the following format:
{"answer": "<ANSWER_TEXT>", "citations": ["<FACT_ID1>", "<FACT_ID2>"]}

If unknown, respond with:
{"answer": "unknown", "citations": []}
"""

question = "การบาดเจ็บจากอุบัติเหตุที่ข้อต่อขากรรไกรสามารถทำให้อาการปวดร้าวไปที่ใดเมื่อเคี้ยวอาหาร?"

facts = """\
[1] การบาดเจ็บจากอุบัติเหตุที่ข้อต่อขากรรไกรสามารถทำให้ปวดร้าวไปที่หูเมื่อเคี้ยวอาหาร
[2] ข้อต่อขากรรไกรอักเสบ (TMJ) สามารถทำให้ปวดบริเวณใกล้ติ่งหูและร้าวไปที่หูเมื่อเคี้ยวอาหาร
"""

prompt = f"""{system_prompt}

Question:
{question}

Facts:
{facts}
"""

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    outputs = model.generate(
        inputs,
        max_new_tokens=256,
        do_sample=False,
        temperature=0.0,
    )

generated = outputs[0, inputs.shape[-1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

ThaiLLM/ThaiLLM-30B-IQ

ThaiLLM-30B-IQ (Medical)

Model Description