113 lines
2.7 KiB
Markdown
113 lines
2.7 KiB
Markdown
---
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base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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- translation
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- en-th
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- safetensors
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license: apache-2.0
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language:
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- en
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- th
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pipeline_tag: translation
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---
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# Qwen3-Izy-Translate-EN-TH-0.6B
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This repository contains the `safetensors` weights for **Qwen3-Izy-Translate-EN-TH-0.6B**, a highly efficient, lightweight model fine-tuned for bidirectional English and Thai translation.
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The model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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---
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## 🚀 Model Details
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- **Developed by:** [izyya](https://huggingface.co/izyya)
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- **License:** Apache-2.0
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- **Base Model:** `unsloth/qwen3-0.6b-unsloth-bnb-4bit`
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- **Supported Languages:** English (EN) and Thai (TH)
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- **Format:** Hugging Face `safetensors`
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---
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## 📝 Prompt Templates
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For optimal translation quality, structure your inputs exactly as shown below depending on the translation direction.
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### English to Thai
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```text
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Translate the following English text to Thai.
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### English:
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[Your English text here]
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```
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### Thai to English
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```text
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Translate the following Thai text to English.
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### Thai:
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[Your Thai text here]
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```
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---
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## 🐍 Inference Code (Python / Transformers)
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This snippet demonstrates how to load the model, pipe your translation prompt into it, and cleanly extract the output text.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Replace with your actual Hugging Face repo path if it differs
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model_name = "izyya/Qwen3-Izy-Translate-EN-TH-0.6B"
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# Load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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# Prepare the translation snippet input
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prompt = """
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Translate the following English text to Thai.
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### English:
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But friends, I am so gay, that if I had a wife, I would encourage her to cheat on me.
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"""
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# Conduct text completion
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
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content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
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print("content:", content)
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```
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---
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## ⚡ Acknowledgments
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* Super-accelerated and fine-tuned with ❤️ by [Unsloth](https://github.com/unslothai/unsloth). |