Bidirectional Thai ⇄ English translation model, fine-tuned from Qwen3-4B via rationale-free distillation from a large reasoning teacher (DeepSeek-V4-Flash).

This model is part of the Kord Translate ENTH V2 family, accompanying the paper "Teaching the Student to Skip the Homework: Rationale-Free Distillation for Thai-English Translation" (KordAI, 2026). Other models in the family: 1.7B, 8B, mBART50.

Model Description

Base model: Qwen3-4B
Adaptation: LoRA (rank 8, alpha 16, dropout 0.02) applied to all attention and MLP projection matrices, trained in 4-bit precision with gradient checkpointing (Unsloth)
Training data: KordAI/Translation-Pairs-8K — ~8,000 bidirectional Thai/English pairs generated by prompting DeepSeek-V4-Flash through an explicit 4-stage reasoning procedure (literal meaning → genre/formality → vocabulary/honorifics → natural rewrite), keeping only the final translation and discarding the reasoning trace
Loss masking: assistant-only, so gradients only flow through the translation output, not the system prompt or source text
Epochs: 3, LoRA learning rate 2e-4 (cosine decay, 5 warmup steps), paged AdamW 8-bit optimizer
Compute: 1× NVIDIA Tesla T4 (16GB), per-device batch 4, gradient accumulation 32

Results (FLORES devtest, 1,012 samples/direction)

Direction	Model	BLEU	chrF	chrF++	BERTScore-P	BERTScore-F1	COMET
en→th	Qwen3-4B (base)	8.87	48.19	39.87	0.82	0.82	0.86
en→th	Kord Translate 4B	9.46	49.01	40.57	0.82	0.82	0.86
th→en	Qwen3-4B (base)	26.94	56.79	54.39	0.95	0.95	0.87
th→en	Kord Translate 4B	25.74	55.79	53.34	0.95	0.95	0.87

Rationale-free distillation produces a small but clear BLEU/chrF gain on en→th (+0.59 BLEU), while th→en is essentially flat to slightly down relative to the untuned Qwen3-4B base — consistent with the paper's finding that gains shrink as base model competence increases. See the paper for comparison against other scales and specialized Thai-English systems.

Inference

This is a chat/instruction-tuned model. Prompt with a system message asking for translation and a user message containing the source text.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "KordAI/Kord-Translate-ENTH-V2-4B"

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

def translate(text: str, direction: str = "en2th") -> str:
    """direction: 'en2th' or 'th2en'"""
    src_lang, tgt_lang = ("English", "Thai") if direction == "en2th" else ("Thai", "English")
    messages = [
        {
            "role": "system",
            "content": (
                f"You are a professional {src_lang}-{tgt_lang} translator. "
                f"Translate the user's text from {src_lang} to {tgt_lang}. "
                "Output only the translation, with no explanation, notes, or extra text."
            ),
        },
        {"role": "user", "content": text},
    ]

    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output_ids = model.generate(
            **inputs,
            max_new_tokens=256,
            do_sample=False,
            temperature=None,
            top_p=None,
            top_k=None,
        )

    generated = output_ids[0][inputs["input_ids"].shape[-1]:]
    return tokenizer.decode(generated, skip_special_tokens=True).strip()


print(translate("How is the weather today in Bangkok?", direction="en2th"))
print(translate("วันนี้อากาศที่กรุงเทพเป็นอย่างไรบ้าง", direction="th2en"))

Using Unsloth (faster 4-bit inference, matches training setup):

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="KordAI/Kord-Translate-ENTH-V2-4B",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)  # enable native 2x faster inference

messages = [
    {"role": "system", "content": "You are a professional English-Thai translator. Translate the user's text from English to Thai. Output only the translation, with no explanation, notes, or extra text."},
    {"role": "user", "content": "How is the weather today in Bangkok?"},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to("cuda")

output_ids = model.generate(input_ids=inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(output_ids[0][inputs.shape[-1]:], skip_special_tokens=True))

Sample Translations

English → Thai

Source

Translation

Ring also settled a lawsuit with competing security company, the ADT Corporation.

ริงยังได้ตัดสินพิจารณาคดีกับบริษัทความปลอดภัยที่แข่งขันกันอย่าง ADT Corporation

USA Gymnastics and the USOC have the same goal — making the sport of gymnastics, and others, as safe as possible for athletes to follow their dreams in a safe, positive and empowered environment.

USA Gymnastics และ USOC มีเป้าหมายเดียวกัน คือ การทำให้กีฬากรีฑศาสตร์และกีฬาอื่นๆ เป็นกีฬาที่ปลอดภัยที่สุดเท่าที่จะทำได้สำหรับนักกีฬาที่จะได้ไปตามฝันของพวกเขาในสภาพแวดล้อมที่ปลอดภัย บวก สร้างสรรค์ และมีอำนาจ

Thai → English

Source	Translation
แกงอาจมีทั้งชนิด "แห้ง" หรือ "น้ำ" ขึ้นอยู่กับปริมาณของเหลว	There are both dry and wet types of curry, depending on the amount of liquid used.
เนื่องจากมีหมู่เกาะให้เลือกถึง 17,000 เกาะ คำว่าอาหารอินโดนีเซียจึงเป็นคำเรียกกว้าง ๆ ที่ครอบคลุมถึงอาหารประจำภูมิภาคทั่วประเทศ	Because there are 17,000 islands to choose from, the term "Indonesian food" is a broad term that encompasses the cuisine of all regions in the country.

Limitations

Trained on a small (~8K pair), single-teacher distillation set; may not generalize to document-level or highly colloquial Thai.
Evaluated only on FLORES devtest (sentence-level general-domain text).
Gains over the untuned Qwen3-4B base are modest and direction-dependent (positive on en→th, roughly flat on th→en).

Citation

bibtex

@article{kordai2026rationalefree,
  title   = {Teaching the Student to Skip the Homework: Rationale-Free Distillation for Thai-English Translation},
  author  = {Jangjit, Naphon and Komsang, Jeerawat and Boran, Kord C.},
  year    = {2026},
  organization = {KordAI}
}

Acknowledgements

Built on Qwen3, with teacher supervision from DeepSeek-V4. LoRA fine-tuning follows Hu et al., 2021 and the 4-bit recipe popularized by QLoRA.

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.