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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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model-index: |
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- name: QLoRA-Flan-T5-Small |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# QLoRA-Flan-T5-Small |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the cnn_dailymail dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## How to use model |
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1. Loading the model |
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'''python |
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import torch |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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# Load peft config for pre-trained checkpoint etc. |
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peft_model_id = "emonty777/QLoRA-Flan-T5-Small" |
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config = PeftConfig.from_pretrained(peft_model_id) |
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# load base LLM model and tokenizer / runs on CPU |
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model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# load base LLM model and tokenizer for GPU |
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model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map={"":0}) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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# Load the Lora model |
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model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0}) |
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model.eval() |
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''' |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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Evaluated on full CNN Dailymail test set |
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'rouge-1': {'r': 0.3484396421841008, 'p': 0.37845620239152916, 'f': 0.3484265780526604}, |
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'rouge-2': {'r': 0.1472418310455188, 'p': 0.15418276080118026, 'f': 0.14343059577230782}, |
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'rouge-l': {'r': 0.3280567401095563, 'p': 0.3565504002457199, 'f': 0.32809541498574013} |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.3 |
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