Instructions to use qwopqwop/EEVE-ALMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use qwopqwop/EEVE-ALMA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0") model = PeftModel.from_pretrained(base_model, "qwopqwop/EEVE-ALMA") - Notebooks
- Google Colab
- Kaggle
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README.md
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base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
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---
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``` python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import prepare_model_for_kbit_training, PeftModel, PeftConfig
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model_path = 'yanolja/EEVE-Korean-10.8B-v1.0'
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lora_path = '
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bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)
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model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=bnb_config, trust_remote_code=True)
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base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
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---
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靷毄 雿办澊韯办厠: aihub
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頉堧牗 頇橁步: RTX3090 x 8
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epoch: 1
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time: 19鞁滉皠
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``` python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import prepare_model_for_kbit_training, PeftModel, PeftConfig
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model_path = 'yanolja/EEVE-Korean-10.8B-v1.0'
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lora_path = 'qwopqwop/EEVE-ALMA'
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bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)
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model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=bnb_config, trust_remote_code=True)
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