---

base_model: manotham/Thai-dialogue-translate_emotion

tags:

• text-generation
• transformers
• unsloth
• qwen3
• translation
• emotion-control
• dpo

license: apache-2.0

language:

• en
• th

---

🎭 Thai Dialogue Emotion-Aware Translator

A Qwen3-based fine-tuned model specialized for translating English dialogue into natural Thai while preserving emotional tone and cinematic style.

This model was trained using Unsloth and further optimized with DPO (Direct Preference Optimization) to improve translation naturalness and emotion alignment.

---

🚀 Highlights

• Developer: manotham
• Base Model: Qwen3
• Training Framework: Unsloth + HuggingFace TRL
• Optimization: Post-DPO alignment
• Task: English → Thai dialogue translation
• Special Feature: Emotion-controlled translation

Supported emotions include:

• sadness
• anger
• contempt
• frustration
• joy
• neutral
• gratitude
• love

and more following tabularisai/multilingual-emotion-classification.

The model is especially suitable for:

• Games
• Visual novels
• Cinematic dialogue
• Storytelling
• Fantasy / Anime-style conversations

---

🛠 Usage

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="manotham/Thai-dialogue-translate_emotion_mdpov2_ckp269",
    max_seq_length=1024,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

def translate_with_emotion(text, emotion):

    messages = [
        {
            "role": "user",
            "content": f"Translate this English sentence into natural Thai.\n[Emotion: {emotion}]\n{text}"
        }
    ]

    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to("cuda")

    with torch.inference_mode():
        outputs = model.generate(
            inputs,
            max_new_tokens=128,
        )

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

print(
    translate_with_emotion(
        "I found you in this broken world.",
        "joy"
    )
)

---

📌 Example Outputs

Sadness

English

I don’t want to save this world anymore… I just want to stay by your side until the end.

Thai

ฉันไม่อยากช่วยโลกใบนี้อีกแล้ว ฉันแค่อยากอยู่ข้างๆ เธอจนถึงท้ายที่สุดเท่านั้น

---

Anger

English

If justice only chooses the strong, then I’ll become the monster this world fears most.

Thai

ถ้าความยุติธรรมเลือกคนที่แข็งแกร่งเท่านั้น ฉันก็จะกลายเป็นสัตว์ประหลาดที่โลกนี้กลัวที่สุด

---

Contempt

English

This is your so-called hero? I’ve seen weaker NPCs in a beginner dungeon.

Thai

นี่คือฮีโร่ที่พวกแกอ้างถึงงั้นเหรอ? ข้าเคยเห็น NPC ที่อ่อนแอกว่านี้ในดันเจี้ยนระดับมือใหม่เลยนะ

---

⚠ Limitations

• The model is optimized for dialogue-style translation, not formal documents.
• Some outputs may become overly dramatic depending on the emotion tag.
• Performance may vary for highly technical or domain-specific content.
• The model may occasionally overfit to fantasy/anime dialogue style.

---

📚 Training Notes

• Fine-tuned using LoRA with Unsloth
• Preference alignment using DPO
• Quantization-friendly for low VRAM GPUs
• Optimized and tested on consumer GPUs