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---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- generator
metrics:
- bleu
- rouge
model-index:
- name: Mistral-7B-Instruct-v0.2-advisegpt-v0.5
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-v0.2-advisegpt-v0.5
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.
It achieves the following results on the evaluation set:
- Loss: 0.0840
- 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}
- Rouge: {'rouge1': 0.9741038510844018, 'rouge2': 0.9550445541823809, 'rougeL': 0.9723656951648176, 'rougeLsum': 0.9736935611588988}
- 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Bleu | Exact Match | Validation Loss | Rouge |
|:-------------:|:------:|:----:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:|
| 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} |
| 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} |
| 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} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |