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

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:

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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