Mistral-7B-Instruct-v0.2-advisegpt-v0.6

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset. It achieves the following results on the evaluation set:

• Loss: 0.0767
• Bleu: {'bleu': 0.9584832765902116, 'precisions': [0.9778312591422885, 0.9625878953932084, 0.9518774970032065, 0.9430684559898991], 'brevity_penalty': 0.9997177244264667, 'length_ratio': 0.9997177642587203, 'translation_length': 1289338, 'reference_length': 1289702}
• Rouge: {'rouge1': 0.9761023152523122, 'rouge2': 0.9590922549283836, 'rougeL': 0.9747297976860183, 'rougeLsum': 0.9758442544146716}
• 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: 3
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 10
• total_train_batch_size: 30
• 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
0.067	0.9998	809	0.0945	{'bleu': 0.9492918853166353, 'precisions': [0.9733554685833311, 0.9543042005762523, 0.9412361771045687, 0.9307382413966919], 'brevity_penalty': 0.9994904502180469, 'length_ratio': 0.9994905799944483, 'translation_length': 1289045, 'reference_length': 1289702}	{'rouge1': 0.9712558044405124, 'rouge2': 0.9500703853191179, 'rougeL': 0.9690578078497468, 'rougeLsum': 0.9708044674114953}	{'exact_match': 0.0}
0.0527	1.9995	1618	0.0779	{'bleu': 0.9568445996007577, 'precisions': [0.977026202258449, 0.961055539100332, 0.9498195483213825, 0.9405540074014527], 'brevity_penalty': 0.9998193217903225, 'length_ratio': 0.9998193381106644, 'translation_length': 1289469, 'reference_length': 1289702}	{'rouge1': 0.9753094821779227, 'rouge2': 0.9574822736836266, 'rougeL': 0.9737984768450723, 'rougeLsum': 0.9750220632065946}	{'exact_match': 0.0}
0.0471	2.9993	2427	0.0767	{'bleu': 0.9584832765902116, 'precisions': [0.9778312591422885, 0.9625878953932084, 0.9518774970032065, 0.9430684559898991], 'brevity_penalty': 0.9997177244264667, 'length_ratio': 0.9997177642587203, 'translation_length': 1289338, 'reference_length': 1289702}	{'rouge1': 0.9761023152523122, 'rouge2': 0.9590922549283836, 'rougeL': 0.9747297976860183, 'rougeLsum': 0.9758442544146716}	{'exact_match': 0.0}

Framework versions

• PEFT 0.10.0
• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1