Model save

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- trl
+- sft
+- generated_from_trainer
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+datasets:
+- generator
+metrics:
+- bleu
+- rouge
+model-index:
+- name: Mistral-7B-Instruct-v0.3-advisegpt-v0.1
+  results: []
+---
+
+<!-- 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-v0.1
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.0762
+- 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}
+- Rouge: {'rouge1': 0.9761616288162651, 'rouge2': 0.9590944581779459, 'rougeL': 0.9748018206627191, 'rougeLsum': 0.9758991028742771}
+
+## 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 | Bleu                                                                                                                                                                                                                                                                | Validation Loss | Rouge                                                                                                                       |
+|:-------------:|:------:|:----:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:|
+| 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} |
+| 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} |
+| 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} |
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1