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--- |
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library_name: peft |
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base_model: google/gemma-7b |
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language: |
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- en |
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- ko |
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tags: |
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- translation |
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- gemma |
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--- |
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# Model Card for Model ID |
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## Model Details |
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### Model Description |
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Summarise Korean sentences concisely |
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- **Developed by:** [Kang Seok Ju] |
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- **Contact:** [brildev7@gmail.com] |
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## Training Details |
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### Training Data |
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https://huggingface.co/datasets/traintogpb/aihub-koen-translation-integrated-tiny-100k |
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# Inference Examples |
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``` |
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import os |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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from peft import PeftModel |
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model_id = "google/gemma-7b" |
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peft_model_id = "brildev7/gemma-7b-translation-enko-sft-qlora" |
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quantization_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.float16, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_use_double_quant=False |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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quantization_config=quantization_config, |
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torch_dtype=torch.float16, |
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attn_implementation="flash_attention_2", |
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token=os.environ['HF_TOKEN'], |
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device_map="auto" |
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) |
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model = PeftModel.from_pretrained(model, peft_model_id) |
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tokenizer = AutoTokenizer.from_pretrained(peft_model_id) |
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tokenizer.pad_token_id = tokenizer.eos_token_id |
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# example |
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prompt_template = """Translate the following sentences into Korean language: |
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{} |
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translation: |
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""" |
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sentences = "Apple is facing a crisis in one of its key markets, China, as it is being challenged by local smartphone manufacturers. In a bid to counter the threat, Apple CEO Tim Cook is reportedly planning to visit China to meet with local smartphone manufacturers and discuss a joint investment. Apple is also reportedly considering installing an AI model from Baidu, the Chinese search giant, on its iPhone. The move comes as Apple is facing a price war in China, with local smartphone manufacturers offering steep discounts on their products." |
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texts = prompt_template.format(sentences) |
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inputs = tokenizer(texts, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=1024) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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- μ νμ κ΅μ° μ€λ§νΈν° μ μ‘°μ¬λ€μ λλ°μ μ€κ΅μμ νλμ ν΅μ¬ μμ₯μ μκΈ°λ₯Ό λ§κ³ μλ€. μ΄ μνμ νκ°νκΈ° μν΄ μ νμ μ΅κ³ κ²½μμμΈ ν μΏ‘μ μ€κ΅μ λ°©λ¬Έν΄ νμ§ μ€λ§νΈν° μ μ‘°μ¬λ€κ³Ό μ μ΄ν΄ 곡λ ν¬μλ₯Ό λ
Όμνλ κ²μΌλ‘ μλ €μ‘λ€. μ νμ λν μ€κ΅ μ΅λ κ²μμ¬ λ°μ΄λ(Baidu)μ μΈκ³΅ μ§λ₯(AI) λͺ¨λΈμ μμ΄ν°μ νμ¬νλ κ²μ κ²ν μ€μΈ κ²μΌλ‘ μ ν΄μ‘λ€. μ νμ κ΅λ΄ μ€λ§νΈν° μ μ‘°μ¬λ€μ΄ μμ λ€μ μ νμ κΈν ν μΈμ λ΄λμΌλ©΄μ μ€κ΅μμ κ°κ²©μ μμ μ§λ©΄ν΄ μλ κ²μ΄λ€. |
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# example |
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sentences = "Is it safe to drink milk and eat chicken?" |
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texts = prompt_template.format(sentences) |
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inputs = tokenizer(texts, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=1024) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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- μ°μ μ λκ³ κΈ°λ μμ νκ°μ? |
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# example |
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sentences = "What precautions to take during the bird flu outbreak" |
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texts = prompt_template.format(sentences) |
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inputs = tokenizer(texts, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=1024) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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- μ‘°λ₯ λ
κ° μ ν μ μ΄λ ν μ£Όμ μ¬νμ ν΄μΌ νλμ§ |
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``` |