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--- |
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license: other |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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datasets: |
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- generator |
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metrics: |
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- bleu |
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- rouge |
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model-index: |
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- name: Meta-Llama-3-8B-Instruct-advisegpt-v0.2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Meta-Llama-3-8B-Instruct-advisegpt-v0.2 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6891 |
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- 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} |
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- Rouge: {'rouge1': 0.8797610930416109, 'rouge2': 0.7838158722398209, 'rougeL': 0.8517529678496154, 'rougeLsum': 0.8731754875691802} |
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- Exact Match: {'exact_match': 0.0} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 12 |
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- total_train_batch_size: 60 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Exact Match | |
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|:-------------:|:------:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------:| |
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| 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} | |
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| 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} | |
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| 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} | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
