llama-pro-8b-tweet-summarization-gradnorm-0.3

This model is a fine-tuned version of TencentARC/LLaMA-Pro-8B on the dialogstudio dataset. It achieves the following results on the evaluation set:

• Loss: 2.9796
• Rouge Scores: {'rouge1': 93.71888929189157, 'rouge2': 77.8377567936117, 'rougeL': 64.47906852741538, 'rougeLsum': 93.71298018429633}
• Bleu Scores: [0.9470990193868204, 0.9341779145832757, 0.9064440397746264, 0.8744914403659334]
• Gen Len: 463.0182

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 7
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge Scores	Bleu Scores	Gen Len
1.9065	1.0	220	1.8530	{'rouge1': 92.83694737064799, 'rouge2': 78.72458121869542, 'rougeL': 67.88788283384865, 'rougeLsum': 92.83768512059282}	[0.8739198483584956, 0.8530170264142946, 0.8271978418182495, 0.7998377773703629]	463.0182
1.6363	2.0	440	1.8633	{'rouge1': 93.54135671371444, 'rouge2': 78.96116387599493, 'rougeL': 67.77857901494997, 'rougeLsum': 93.54432289584433}	[0.8758125801988195, 0.8577741180618648, 0.8322886881519586, 0.80457236049974]	463.0182
1.2817	3.0	660	2.0098	{'rouge1': 87.30764070509844, 'rouge2': 73.12328274037898, 'rougeL': 62.00625532521349, 'rougeLsum': 87.29149649901954}	[0.8757949025917542, 0.8593181834244542, 0.8334473061685955, 0.8048319452251607]	463.0182
0.9049	4.0	880	2.2481	{'rouge1': 87.35996946418575, 'rouge2': 72.87802745947901, 'rougeL': 61.35206821444361, 'rougeLsum': 87.32662841081371}	[0.8755472589597261, 0.859572654041077, 0.8333237300074641, 0.804082483213136]	463.0182
0.5916	5.0	1100	2.5061	{'rouge1': 78.38431994557745, 'rouge2': 64.89809559762811, 'rougeL': 53.805209482421525, 'rougeLsum': 78.30608179426231}	[0.747179346815877, 0.7352208249958618, 0.7126103103040894, 0.6869428956670465]	463.0182
0.3898	6.0	1320	2.8150	{'rouge1': 93.77539618029996, 'rouge2': 78.03050568501187, 'rougeL': 64.82344374456906, 'rougeLsum': 93.76894400818286}	[0.9469183628254614, 0.9342162110956728, 0.9067374010427977, 0.8750430150656403]	463.0182
0.2961	7.0	1540	2.9796	{'rouge1': 93.71888929189157, 'rouge2': 77.8377567936117, 'rougeL': 64.47906852741538, 'rougeLsum': 93.71298018429633}	[0.9470990193868204, 0.9341779145832757, 0.9064440397746264, 0.8744914403659334]	463.0182

Framework versions

• PEFT 0.8.2.dev0
• Transformers 4.38.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.16.2.dev0
• Tokenizers 0.15.1