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--- |
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license: apache-2.0 |
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datasets: |
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- squarelike/sharegpt_deepl_ko_translation |
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language: |
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- en |
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- ko |
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pipeline_tag: translation |
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--- |
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# Gugugo-koen-7B-V1.1 |
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Detail repo: [https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo) |
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![Gugugo](./logo.png) |
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**Base Model**: [Llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b) |
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**Training Dataset**: [sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/sharegpt_deepl_ko_translation). |
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I trained with 1x A6000 GPUs for 90 hours. |
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## **Prompt Template** |
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**KO->EN** |
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``` |
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### νκ΅μ΄: {sentence}</λ> |
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### μμ΄: |
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``` |
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**EN->KO** |
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``` |
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### μμ΄: {sentence}</λ> |
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### νκ΅μ΄: |
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``` |
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## **Implementation Code** |
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```python |
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from vllm import LLM, SamplingParams |
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def make_prompt(data): |
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prompts = [] |
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for line in data: |
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prompts.append(f"### μμ΄: {line}</λ>\n### νκ΅μ΄:") |
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return prompts |
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texts = [ |
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"Hello world!", |
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"Nice to meet you!" |
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] |
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prompts = make_prompt(texts) |
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sampling_params = SamplingParams(temperature=0.01, stop=["</λ>"], max_tokens=700) |
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llm = LLM(model="squarelike/Gugugo-koen-7B-V1.1-AWQ", quantization="awq", dtype="half") |
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outputs = llm.generate(prompts, sampling_params) |
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# Print the outputs. |
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for output in outputs: |
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print(output.outputs[0].text) |
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``` |