base_model, tags, license, language, pipeline_tag

base_model	tags	license	language	pipeline_tag
unsloth/qwen3-0.6b-unsloth-bnb-4bit	text-generation-inference transformers unsloth qwen3 translation en-th safetensors	apache-2.0	en th	translation

Qwen3-Izy-Translate-EN-TH-0.6B

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.

The model was trained 2x faster with Unsloth and Hugging Face's TRL library.

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🚀 Model Details

• Developed by: izyya
• License: Apache-2.0
• Base Model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
• Supported Languages: English (EN) and Thai (TH)
• Format: Hugging Face safetensors

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📝 Prompt Templates

For optimal translation quality, structure your inputs exactly as shown below depending on the translation direction.

English to Thai

Translate the following English text to Thai. 
### English:
[Your English text here]

Thai to English

Translate the following Thai text to English. 
### Thai:
[Your Thai text here]

---

🐍 Inference Code (Python / Transformers)

This snippet demonstrates how to load the model, pipe your translation prompt into it, and cleanly extract the output text.

from transformers import AutoModelForCausalLM, AutoTokenizer

# Replace with your actual Hugging Face repo path if it differs
model_name = "izyya/Qwen3-Izy-Translate-EN-TH-0.6B"

# Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Prepare the translation snippet input
prompt = """
Translate the following English text to Thai. 

### English:
But friends, I am so gay, that if I had a wife, I would encourage her to cheat on me.
"""

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)

output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

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⚡ Acknowledgments

• Super-accelerated and fine-tuned with ❤️ by Unsloth.