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metadata
base_model: meta-llama/Llama-2-7b-hf
tags:
  - generated_from_trainer
model-index:
  - name: Llama2-7bn-xsum-adapter
    results: []
datasets:
  - EdinburghNLP/xsum
language:
  - en
pipeline_tag: summarization
metrics:
  - rouge

Llama2-7bn-xsum-adapter

Weights & Biases runs for training and evaluation are available for a detailed overview!

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on a XSum dataset with Causal LM task. You can view all the implementation details on the GitHub project

Weights & Biases Training and Evaluation Documentation

See the training and evaluation on Weights & Biases for more details!

Summary table of final metrics:

Metric rouge1 rouge2 rougeL FactCC ANLI SummaC BARTScore
Mean 0.18 0.033 0.126 0.188 0.408 0.658 -3.713
Std 0.09 0.049 0.067 0.317 0.462 0.247 0.831

Training procedure

Causal language modeling. Nesting the summary paragraph in a prompt: {Summarize this article: ''; Summary: }

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • lr_scheduler_warmup_steps: 450.5
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.0.1
  • Datasets 2.14.6
  • Tokenizers 0.14.1