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metadata
license: other
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
  - generator
metrics:
  - bleu
  - rouge
model-index:
  - name: Meta-Llama-3-8B-Instruct-advisegpt-v0.2
    results: []

Meta-Llama-3-8B-Instruct-advisegpt-v0.2

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6891
  • Bleu: {'bleu': 0.7794801643070653, 'precisions': [0.8826931860836374, 0.7921738670614986, 0.7521498106470706, 0.7302911239298923], 'brevity_penalty': 0.9901418189906349, 'length_ratio': 0.9901900930687305, 'translation_length': 663363, 'reference_length': 669935}
  • Rouge: {'rouge1': 0.8797610930416109, 'rouge2': 0.7838158722398209, 'rougeL': 0.8517529678496154, 'rougeLsum': 0.8731754875691802}
  • Exact Match: {'exact_match': 0.0}

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: 2e-05
  • train_batch_size: 5
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 12
  • total_train_batch_size: 60
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Bleu Rouge Exact Match
0.1221 0.9967 175 0.6891 {'bleu': 0.7794801643070653, 'precisions': [0.8826931860836374, 0.7921738670614986, 0.7521498106470706, 0.7302911239298923], 'brevity_penalty': 0.9901418189906349, 'length_ratio': 0.9901900930687305, 'translation_length': 663363, 'reference_length': 669935} {'rouge1': 0.8797610930416109, 'rouge2': 0.7838158722398209, 'rougeL': 0.8517529678496154, 'rougeLsum': 0.8731754875691802} {'exact_match': 0.0}
0.1091 1.9991 351 0.6977 {'bleu': 0.7805322713844085, 'precisions': [0.8833412231532545, 0.7931277801953389, 0.7535080094374768, 0.7317717661200727], 'brevity_penalty': 0.9900498636013274, 'length_ratio': 0.990099039459052, 'translation_length': 663302, 'reference_length': 669935} {'rouge1': 0.88033924999596, 'rouge2': 0.7849601251129642, 'rougeL': 0.8519921287058778, 'rougeLsum': 0.8736913571890462} {'exact_match': 0.0}
0.1067 2.9900 525 0.7051 {'bleu': 0.7808878497559923, 'precisions': [0.8838378429742967, 0.7938818670645449, 0.7542948740286441, 0.7326395901316979], 'brevity_penalty': 0.9895748787367024, 'length_ratio': 0.9896288445894005, 'translation_length': 662987, 'reference_length': 669935} {'rouge1': 0.8806020535666979, 'rouge2': 0.7857024053578856, 'rougeL': 0.8520805662216797, 'rougeLsum': 0.8739154999822791} {'exact_match': 0.0}

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1