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Mistral-7B-Instruct-v0.3-advisegpt-v0.4
ninyx/Mistral-7B-Instruct-v0.3-advisegpt-v0.4
Mistral-7B-Instruct-v0.3-advisegpt-v0.4 is a machine learning model from ninyx. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jun 14, 2024
- Updated
- Jun 17, 2024
- SHA
- 4d6d953a
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
Mistral-7B-Instruct-v0.3-advisegpt-v0.4
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.0776
- Bleu: {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387}
- Rouge: {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228}
- 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 15
- total_train_batch_size: 15
- 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 |
|---|---|---|---|---|---|---|
| 0.0592 | 0.9998 | 2664 | 0.0792 | {'bleu': 0.957140829496306, 'precisions': [0.9770110285842899, 0.9611535701983837, 0.9499650178830994, 0.9408134298916666], 'brevity_penalty': 1.0, 'length_ratio': 1.0000945396593872, 'translation_length': 666450, 'reference_length': 666387} | {'rouge1': 0.9756420869808171, 'rouge2': 0.958253583847128, 'rougeL': 0.9741670140375769, 'rougeLsum': 0.9753898276329086} | {'exact_match': 0.0} |
| 0.0518 | 2.0000 | 5329 | 0.0776 | {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387} | {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228} | {'exact_match': 0.0} |
| 0.0439 | 2.9994 | 7992 | 0.0830 | {'bleu': 0.9593680325138967, 'precisions': [0.97789654044549, 0.9630261327317164, 0.9526617494511856, 0.9442157972615742], 'brevity_penalty': 1.0, 'length_ratio': 1.0001725723941193, 'translation_length': 666502, 'reference_length': 666387} | {'rouge1': 0.9766709553577743, 'rouge2': 0.9604006931620985, 'rougeL': 0.9753845279467352, 'rougeLsum': 0.9764641972952484} | {'exact_match': 0.0} |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- Jun 14, 2024
- Updated
- Jun 17, 2024