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--- |
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library_name: peft |
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base_model: ericzzz/falcon-rw-1b-instruct-openorca |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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## Model Description |
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Quick and dirty hack for binary movie sentiment analysis. |
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Finetuned with LoRA (PEFT) on [ericzzz/falcon-rw-1b-instruct-openorca](https://huggingface.co/datasets/open-llm-leaderboard/details_ericzzz__falcon-rw-1b-instruct-openorca). |
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Trained on a subset of [IMDB Dataset of 50K Movie Reviews](https://www.kaggle.com/datasets/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) from Kaggle: |
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**To load the model you can use this code:** |
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PEFT_MODEL = "Jonny00/falcon-1b-movie-sentiment-analysis" |
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config = PeftConfig.from_pretrained(PEFT_MODEL) |
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model = AutoModelForCausalLM.from_pretrained( |
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config.base_model_name_or_path, |
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return_dict=True, |
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device_map="auto", |
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trust_remote_code=True) |
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tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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tokenizer.pad_token = tokenizer.eos_token |
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model = PeftModel.from_pretrained(model, PEFT_MODEL) |
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**Input**: *("\<human\>: This movie sucks, I'd rather stay at home! \<assistant\>:")* |
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**Output**: *("... negative \<assistant\>: negative \<assistant\>: negative ...")* |
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## Example Google Colab Code |
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https://colab.research.google.com/drive/1LUILztSocpqpMz8xACbtmxl-W-cORXRZ?usp=sharing |
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## Framework versions |
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- PEFT 0.7.1 |
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