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-v0.2
+  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-v0.2
+
+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: 0.0780
+- Bleu: {'bleu': 0.9576887647563643, 'precisions': [0.9773669728326163, 0.9618578951912286, 0.9507543139197927, 0.9415539534224628], 'brevity_penalty': 0.9998937877305732, 'length_ratio': 0.9998937933706971, 'translation_length': 696681, 'reference_length': 696755}
+- Rouge: {'rouge1': 0.9756788083049949, 'rouge2': 0.9583995226740446, 'rougeL': 0.9744286269286386, 'rougeLsum': 0.9754176834545093}
+- 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: 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: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Bleu                                                                                                                                                                                                                                                              | Exact Match          | Validation Loss | Rouge                                                                                                                       |
+|:-------------:|:------:|:----:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:|
+| 0.0674        | 1.0    | 1272 | {'bleu': 0.9527886605929342, 'brevity_penalty': 1.0, 'length_ratio': 1.0000143520618172, 'precisions': [0.9750579097383083, 0.9573526809414263, 0.9448030782898647, 0.9344211337471139], 'reference_length': 696764, 'translation_length': 696774}                | {'exact_match': 0.0} | 0.0879          | {'rouge1': 0.9733658563180658, 'rouge2': 0.9537375602435975, 'rougeL': 0.9714782202593812, 'rougeLsum': 0.97295717219818}   |
+| 0.0515        | 2.0    | 2544 | {'bleu': 0.9576841610471547, 'brevity_penalty': 0.9998937891025745, 'length_ratio': 0.9998937947425527, 'precisions': [0.9774131966871923, 0.9617936398993365, 0.9507289899724213, 0.9415792951573699], 'reference_length': 696764, 'translation_length': 696690} | {'exact_match': 0.0} | 0.0783          | {'rouge1': 0.9757072387880681, 'rouge2': 0.9584139466483359, 'rougeL': 0.9743902945474832, 'rougeLsum': 0.9754213243935133} |
+| 0.0574        | 2.9993 | 2997 | {'bleu': 0.9566916740680499, 'brevity_penalty': 1.0, 'length_ratio': 1.00018514398892, 'precisions': [0.9768282813208511, 0.960876488636805, 0.9494536267704137, 0.9400012431679968], 'reference_length': 696755, 'translation_length': 696884}                   | {'exact_match': 0.0} | 0.0809          | {'rouge1': 0.9754024081831265, 'rouge2': 0.9579286248562431, 'rougeL': 0.9741313460430334, 'rougeLsum': 0.9751613463738352} |
+| 0.0482        | 3.9993 | 3996 | 0.0808                                                                                                                                                                                                                                                            | {'bleu': 0.9574684676357755, 'precisions': [0.9771731036056137, 0.9615197629595535, 0.950377700460969, 0.9411784261633503], 'brevity_penalty': 1.0, 'length_ratio': 1.0002669517979779, 'translation_length': 696941, 'reference_length': 696755}| {'rouge1': 0.9757795166466966, 'rouge2': 0.9586013928880327, 'rougeL': 0.9745320041915129, 'rougeLsum': 0.9755165129747526}| {'exact_match': 0.0}                                                                                                        |
+| 0.0458        | 4.9986 | 4995 | 0.0847                                                                                                                                                                                                                                                            | {'bleu': 0.9570671016785056, 'precisions': [0.976919456982413, 0.9611588208136713, 0.9499201259530098, 0.9406510563080023], 'brevity_penalty': 1.0, 'length_ratio': 1.0003430187081541, 'translation_length': 696994, 'reference_length': 696755}| {'rouge1': 0.9755632054095464, 'rouge2': 0.9582426903380377, 'rougeL': 0.9743228923598912, 'rougeLsum': 0.9753134364311447}| {'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

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