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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-v0.2 |
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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-v0.2-advisegpt-v0.5 |
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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-v0.2-advisegpt-v0.5 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0840 |
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- Bleu: {'bleu': 0.9537910015397628, 'precisions': [0.9763005593772222, 0.9591762297332277, 0.9471223357463351, 0.9370695448087227], 'brevity_penalty': 0.9989373668126428, 'length_ratio': 0.9989379310075293, 'translation_length': 1022387, 'reference_length': 1023474} |
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- Rouge: {'rouge1': 0.9741038510844018, 'rouge2': 0.9550445541823809, 'rougeL': 0.9723656951648176, 'rougeLsum': 0.9736935611588988} |
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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: 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 | Exact Match | Validation Loss | Rouge | |
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|:-------------:|:------:|:----:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:| |
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| 0.069 | 0.9999 | 829 | {'bleu': 0.9459206747141892, 'brevity_penalty': 0.998656611989374, 'length_ratio': 0.9986575135274565, 'precisions': [0.9726768417963018, 0.9521542081327253, 0.9380288226144853, 0.9265355643009697], 'reference_length': 1023474, 'translation_length': 1022100} | {'exact_match': 0.0} | 0.0990 | {'rouge1': 0.9702189356306301, 'rouge2': 0.9472171244648081, 'rougeL': 0.9677029434775739, 'rougeLsum': 0.9695684693436178} | |
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| 0.0501 | 1.9999 | 1658 | {'bleu': 0.9537910015397628, 'brevity_penalty': 0.9989373668126428, 'length_ratio': 0.9989379310075293, 'precisions': [0.9763005593772222, 0.9591762297332277, 0.9471223357463351, 0.9370695448087227], 'reference_length': 1023474, 'translation_length': 1022387} | {'exact_match': 0.0} | 0.0840 | {'rouge1': 0.9741105562035488, 'rouge2': 0.9550205654651982, 'rougeL': 0.9723363685950056, 'rougeLsum': 0.9737013621980013} | |
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| 0.0479 | 2.9999 | 2487 | 0.0850 | {'bleu': 0.9548514568958526, 'precisions': [0.9767648848122783, 0.9601353822381405, 0.9483682511725553, 0.9385079979703334], 'brevity_penalty': 0.9989676875356347, 'length_ratio': 0.9989682200036347, 'translation_length': 1022418, 'reference_length': 1023474}| {'rouge1': 0.9746456572659052, 'rouge2': 0.9560608145101823, 'rougeL': 0.9729518327172596, 'rougeLsum': 0.9742472834405176}| {'exact_match': 0.0} | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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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 |
