ernlavr/llama-2-7bn-xsum-lora-adapter

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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: '<INPUT_DOCUMENT>'; Summary: <OUTPUT>}

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.001
• —trainbatchsize: 32
• —evalbatchsize: 32
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.1
• —lrschedulerwarmup_steps: 450.5
• —num_epochs: 3
• —mixedprecisiontraining: Native AMP

Framework versions

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