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Model: manotham/Thai-dialogue-translate_emotion_mdpov2_ckp269
Source: Original Platform

README.md

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+
+---
+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
+
+```python
+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