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--- |
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language: |
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- en |
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- ko |
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library_name: peft |
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tags: |
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- translation |
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- gemma |
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base_model: google/gemma-2b |
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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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- **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-2b" |
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peft_model_id = "brildev7/gemma-2b-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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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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- 우유를 마시고, 닭고기를 먹으면 안 됩니까? |
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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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- 바이러스 플루 발생 중 취해야 할 예방 조치 |
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``` |
