Bidirectional Thai ⇄ English translation model, fine-tuned from Qwen3-1.7B 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: 4B, 8B, mBART50.

Model Description

Base model: Qwen3-1.7B
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 2, gradient accumulation 32

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

Direction	Model	BLEU	chrF	chrF++	BERTScore-P	BERTScore-F1	COMET
en→th	Qwen3-1.7B (base)	8.00	42.20	35.21	0.80	0.79	0.81
en→th	Kord Translate 1.7B	8.27	42.68	35.37	0.94	0.94	0.85
th→en	Qwen3-1.7B (base)	22.11	52.84	50.39	0.94	0.94	0.85
th→en	Kord Translate 1.7B	22.18	52.09	49.62	0.94	0.94	0.86

At this scale, rationale-free distillation produces small, consistent improvements over the untuned Qwen3-1.7B base in both directions, most notably in BERTScore and COMET on en→th. See the paper for comparison against larger 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-V2-1.7B"

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-V2-1.7B",
    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
Efforts to search for the crash site are being met by bad weather and harsh terrain.	ความพยายามในการค้นหาที่เกิดเหตุยังคงถูกขัดขวางด้วยสภาพอากาศแย่และภูเขาที่รุนแรง
They are cooler than the surrounding surface in the day and warmer at night.	พวกมันเย็นกว่าพื้นผิวรอบๆ ในช่วงกลางวัน และร้อนกว่าในช่วงกลางคืน

Thai → English

Source	Translation
ระหว่างเดินทาง อิวาซากิประสบปัญหาหลายประการ	While on the way, Ivaizaki encountered several problems.
วันนี้เมื่อเวลา 12.00 น. ตามเวลามาตรฐานสากล กลุ่ม Iraq Study Group ได้นำเสนอรายงานของพวกเขา	At 12:00 noon local time, the Iraq Study Group presented their report.

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).
Smallest model in the family — larger scales (4B, 8B) achieve higher absolute translation quality; this 1.7B model favors low compute/latency over peak quality.

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.