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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_lora2
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. -->
# lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_lora2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4374
- Accuracy: 0.6510
## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.168 | 1.0 | 187 | 1.1156 | 0.6787 |
| 1.0838 | 2.0 | 375 | 1.1152 | 0.6790 |
| 0.9936 | 3.0 | 562 | 1.1289 | 0.6781 |
| 0.8717 | 4.0 | 750 | 1.1731 | 0.6742 |
| 0.7555 | 5.0 | 937 | 1.2357 | 0.6711 |
| 0.6292 | 6.0 | 1125 | 1.3174 | 0.6674 |
| 0.5073 | 7.0 | 1312 | 1.4164 | 0.6638 |
| 0.4021 | 8.0 | 1500 | 1.4974 | 0.6621 |
| 0.3017 | 9.0 | 1687 | 1.6756 | 0.6576 |
| 0.2292 | 10.0 | 1875 | 1.8038 | 0.6548 |
| 0.1766 | 11.0 | 2062 | 1.9233 | 0.6539 |
| 0.1406 | 12.0 | 2250 | 2.0465 | 0.6519 |
| 0.1146 | 13.0 | 2437 | 2.1205 | 0.6518 |
| 0.0988 | 14.0 | 2625 | 2.1974 | 0.6523 |
| 0.0915 | 15.0 | 2812 | 2.2592 | 0.6519 |
| 0.0859 | 16.0 | 3000 | 2.3234 | 0.6515 |
| 0.0785 | 17.0 | 3187 | 2.3664 | 0.6515 |
| 0.0761 | 18.0 | 3375 | 2.3999 | 0.6507 |
| 0.0767 | 19.0 | 3562 | 2.4329 | 0.6517 |
| 0.0756 | 19.95 | 3740 | 2.4374 | 0.6510 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
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