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Model: trillionlabs/Tri-1.8B-Translation
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- translation
+- multilingual
+- trillionlabs
+- preview
+license: apache-2.0
+language:
+- ko
+- en
+- zh
+- ja
+---
+
+# Tri-1.8B Translation
+
+We release **Tri-1.8B Translation**, a lightweight multilingual translation model from **Trillion Labs**.  
+
+Tri-1.8B Translate is trained through pretraining and supervised fine-tuning (SFT), and was distilled from our larger Tri-21B model to preserve strong translation quality in a much smaller, deployment-friendly 1.8B parameter model. It supports all translation directions among English, Korean, Japanese, and Chinese.
+
+
+
+## ✨ Highlights
+
+* **Compact & efficient:** \~1.8B params, easy to serve on a single GPU.
+* **Multilingual:** Fully bidirectional **EN ↔ KO ↔ JA ↔ ZH**.
+* **Simple prompts:** Works with a short **task instruction + `<lang>` tag**.
+* **Research-ready:** Suitable for domain SFT or lightweight adapters.
+
+---
+
+## 🧾 Prompt format
+
+```
+Translate the following {SRC_NAME} text into {TGT_NAME}:
+{TEXT} <{lang_tag}>
+```
+
+Where `{lang_tag} ∈ { en, ko, ja, zh }`.
+
+---
+
+## 🔧 Usage
+
+### 1) 🤗 Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "trillionlabs/Tri-1.8B-Translation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+text = "안녕하세요"
+messages = [
+    {"role": "user", "content": f"Translate the following Korean text into English:\n{text} <en>"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages, 
+    return_tensors="pt",
+    add_generation_prompt=True
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=256,
+    do_sample=False,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+translation = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+
+print(f"Korean: {text}")
+print(f"English: {translation}")
+```
+
+---
+
+### 2) Local vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="trillionlabs/Tri-1.8B-Translation")
+sp = SamplingParams(temperature=0.3, max_tokens=512)
+
+def translate(text, target="en"):
+    prompt = f"Translate into {target}:\n{text} <{target}>"
+    out = llm.chat([{"role": "user", "content": prompt}], sampling_params=sp)
+    return out[0].outputs[0].text.strip()
+
+print(translate("안녕하세요", "en"))
+```
+
+---
+
+### 3) API client (OpenAI-compatible)
+
+```python
+import openai
+
+client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+
+def translate(text, target="en"):
+    prompt = f"Translate into {target}:\n{text} <{target}>"
+    resp = client.chat.completions.create(
+        model="trillionlabs/Tri-1.8B-Translation",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.3,
+        max_tokens=512,
+    )
+    return resp.choices[0].message.content.strip()
+
+print(translate("안녕하세요", "en"))
+```
+
+
+## 📜 License
+
+Apache-2.0 (for model weights & code). Please verify data licenses for your use.
+
+
+## 🙏 Acknowledgments
+
+* Thanks to the **ByteDance Seed team** for releasing **Seed-X**; our prompt template and some training design were adapted from their paper.
+
+
+## 📚 Citation
+
+If you use **Tri-1.8B Translation**, please cite:
+
+```bibtex
+@misc{suk2025tri18b,
+  title   = {Tri-1.8B Translation: A Lightweight Multilingual Translation Model},
+  author  = {Juyoung Suk and Trillion Labs},
+  year    = {2025},
+  howpublished = {\url{https://huggingface.co/trillionlabs/Tri-1.8B-Translation}}
+}
+```