File size: 6,474 Bytes
e587852 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
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-v0.2
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-v0.2-advisegpt-v0.2
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: 0.2663
- Bleu: {'bleu': 0.8973677595827475, 'precisions': [0.9445357190260831, 0.9043357485277211, 0.884048801354545, 0.8699324916460348], 'brevity_penalty': 0.9967654892802698, 'length_ratio': 0.9967707090483877, 'translation_length': 1235588, 'reference_length': 1239591}
- Rouge: {'rouge1': 0.94002209733584, 'rouge2': 0.8959242425644911, 'rougeL': 0.931506639182089, 'rougeLsum': 0.9379602925725274}
- 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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Exact Match |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------:|
| 0.0486 | 1.0 | 953 | 0.2746 | {'bleu': 0.8908185969537198, 'precisions': [0.9404575669873605, 0.8973236909340203, 0.8757372017868406, 0.8612917392143906], 'brevity_penalty': 0.9973237695370935, 'length_ratio': 0.9973273442611313, 'translation_length': 1236278, 'reference_length': 1239591} | {'rouge1': 0.9362204684890618, 'rouge2': 0.8885056700228431, 'rougeL': 0.9265631818483623, 'rougeLsum': 0.9336844419843153} | {'exact_match': 0.0} |
| 0.0491 | 2.0 | 1906 | 0.2663 | {'bleu': 0.8973677595827475, 'precisions': [0.9445357190260831, 0.9043357485277211, 0.884048801354545, 0.8699324916460348], 'brevity_penalty': 0.9967654892802698, 'length_ratio': 0.9967707090483877, 'translation_length': 1235588, 'reference_length': 1239591} | {'rouge1': 0.94002209733584, 'rouge2': 0.8959242425644911, 'rougeL': 0.931506639182089, 'rougeLsum': 0.9379602925725274} | {'exact_match': 0.0} |
| 0.0457 | 3.0 | 2859 | 0.2713 | {'bleu': 0.9011515587348646, 'precisions': [0.9455932972222963, 0.9073373719165547, 0.8876381124008869, 0.8739477250662966], 'brevity_penalty': 0.9976982094967736, 'length_ratio': 0.9977008545560592, 'translation_length': 1236741, 'reference_length': 1239591} | {'rouge1': 0.9413126529676463, 'rouge2': 0.899299253704354, 'rougeL': 0.9334048668464673, 'rougeLsum': 0.9393911208574579} | {'exact_match': 0.0} |
| 0.0427 | 4.0 | 3812 | 0.2973 | {'bleu': 0.90301748257064, 'precisions': [0.9459337521093097, 0.908889094215937, 0.8900854627247164, 0.8768615812629189], 'brevity_penalty': 0.9977289348603028, 'length_ratio': 0.9977315098286451, 'translation_length': 1236779, 'reference_length': 1239591} | {'rouge1': 0.9416505951538487, 'rouge2': 0.9013278888630185, 'rougeL': 0.9338287357833286, 'rougeLsum': 0.9396676143080369} | {'exact_match': 0.0} |
| 0.0387 | 5.0 | 4765 | 0.3174 | {'bleu': 0.9027081754085503, 'precisions': [0.9453687163030563, 0.9082821849767655, 0.8894881333354993, 0.8763610309353894], 'brevity_penalty': 0.9980126956655654, 'length_ratio': 0.9980146677412146, 'translation_length': 1237130, 'reference_length': 1239591} | {'rouge1': 0.9410039582969136, 'rouge2': 0.9003660562981124, 'rougeL': 0.9333230818742395, 'rougeLsum': 0.9390562109303144} | {'exact_match': 0.0} |
| 0.0375 | 6.0 | 5718 | 0.3550 | {'bleu': 0.9020801569628037, 'precisions': [0.9452423604006518, 0.9080153383434265, 0.889329438008671, 0.8762846622568347], 'brevity_penalty': 0.9974911930257172, 'length_ratio': 0.9974943348249543, 'translation_length': 1236485, 'reference_length': 1239591} | {'rouge1': 0.9405264680129318, 'rouge2': 0.8997693944544493, 'rougeL': 0.9326541656452509, 'rougeLsum': 0.9385618029898112} | {'exact_match': 0.0} |
| 0.0361 | 7.0 | 6671 | 0.3875 | {'bleu': 0.9012292336144602, 'precisions': [0.9445250811645649, 0.907065679940991, 0.8882131409279821, 0.8750920375317651], 'brevity_penalty': 0.9976529282968476, 'length_ratio': 0.99765567836488, 'translation_length': 1236685, 'reference_length': 1239591} | {'rouge1': 0.9398793690117034, 'rouge2': 0.8988761356423343, 'rougeL': 0.9319409400581564, 'rougeLsum': 0.9378780059486569} | {'exact_match': 0.0} |
| 0.0336 | 8.0 | 7624 | 0.4056 | {'bleu': 0.9006875075046153, 'precisions': [0.9440281128914361, 0.906425354183536, 0.8876174257258417, 0.874531174789441], 'brevity_penalty': 0.9976876979719658, 'length_ratio': 0.997690367225964, 'translation_length': 1236728, 'reference_length': 1239591} | {'rouge1': 0.9394107383916215, 'rouge2': 0.8982745501459677, 'rougeL': 0.9313967228794204, 'rougeLsum': 0.9373525606500615} | {'exact_match': 0.0} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 |