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--- |
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license: apache-2.0 |
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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: mistralai/Mistral-7B-Instruct-v0.3 |
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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: Mistral-7B-Instruct-v0.3-advisegpt-v0.1 |
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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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# Mistral-7B-Instruct-v0.3-advisegpt-v0.1 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0762 |
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- Bleu: {'bleu': 0.9585152983456746, 'precisions': [0.9779106264925121, 0.9626412004947897, 0.951895206199588, 0.9430042745426802], 'brevity_penalty': 0.999729358235579, 'length_ratio': 0.9997293948524543, 'translation_length': 1289353, 'reference_length': 1289702} |
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- Rouge: {'rouge1': 0.9761616288162651, 'rouge2': 0.9590944581779459, 'rougeL': 0.9748018206627191, 'rougeLsum': 0.9758991028742771} |
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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: 3 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 10 |
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- total_train_batch_size: 30 |
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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 | Bleu | Validation Loss | Rouge | |
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|:-------------:|:------:|:----:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:| |
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| 0.0675 | 0.9998 | 809 | {'bleu': 0.9495787144110293, 'brevity_penalty': 0.9993236461934566, 'length_ratio': 0.9993238748175935, 'precisions': [0.9735806894625358, 0.9547064588389024, 0.9417775802515341, 0.9313417436570839], 'reference_length': 1289702, 'translation_length': 1288830} | 0.0936 | {'rouge1': 0.9713550622229471, 'rouge2': 0.9502301622796694, 'rougeL': 0.9691228372678113, 'rougeLsum': 0.9708685856330016} | |
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| 0.0548 | 1.9998 | 1618 | 0.0771 | {'bleu': 0.9571495232321637, 'precisions': [0.9773150683231548, 0.9614637013631232, 0.95029828201407, 0.9410538932261553], 'brevity_penalty': 0.9996991100504438, 'length_ratio': 0.9996991553087458, 'translation_length': 1289314, 'reference_length': 1289702}| {'rouge1': 0.9755343391324649, 'rouge2': 0.9577790978374392, 'rougeL': 0.9740177474237091, 'rougeLsum': 0.9752585254668996} | |
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| 0.0439 | 2.9995 | 2427 | 0.0762 | {'bleu': 0.9585152983456746, 'precisions': [0.9779106264925121, 0.9626412004947897, 0.951895206199588, 0.9430042745426802], 'brevity_penalty': 0.999729358235579, 'length_ratio': 0.9997293948524543, 'translation_length': 1289353, 'reference_length': 1289702}| {'rouge1': 0.9761616288162651, 'rouge2': 0.9590944581779459, 'rougeL': 0.9748018206627191, 'rougeLsum': 0.9758991028742771} | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
