metadata
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: zephyr-7b
results: []
zephyr-7b
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4405
- Rewards/chosen: -3.0809
- Rewards/rejected: -4.8915
- Rewards/accuracies: 0.3438
- Rewards/margins: 1.8106
- Logps/rejected: -566.3419
- Logps/chosen: -371.9976
- Logits/rejected: 4.5207
- Logits/chosen: 4.3874
- Use Label: 5649.7188
- Pred Label: 1650.2812
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Use Label | Pred Label |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6535 | 0.21 | 100 | 0.6432 | -0.2049 | -0.3593 | 0.3516 | 0.1544 | -113.1259 | -84.4063 | -2.0537 | -2.0656 | 1713.5 | 18.5 |
0.507 | 0.42 | 200 | 0.5048 | -1.6723 | -2.3466 | 0.3594 | 0.6743 | -311.8494 | -231.1388 | 2.1626 | 2.0915 | 3214.5625 | 373.4375 |
0.4799 | 0.63 | 300 | 0.4885 | -1.7906 | -2.6624 | 0.3359 | 0.8718 | -343.4285 | -242.9698 | 3.2225 | 3.1511 | 4474.75 | 969.25 |
0.4443 | 0.84 | 400 | 0.4405 | -3.0809 | -4.8915 | 0.3438 | 1.8106 | -566.3419 | -371.9976 | 4.5207 | 4.3874 | 5649.7188 | 1650.2812 |
Framework versions
- PEFT 0.7.1
- Transformers 4.38.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2