metadata
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_qa_5e-5_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- name: Accuracy
type: accuracy
value: 0.584886075949367
lmind_hotpot_train8000_eval7405_v1_qa_5e-5_lora2
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset. It achieves the following results on the evaluation set:
- Loss: 3.6692
- Accuracy: 0.5849
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-05
- 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: 50.0
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| 1.798 | 1.0 | 250 | 0.6067 | 1.8213 |
| 1.7 | 2.0 | 500 | 0.6077 | 1.8046 |
| 1.5869 | 3.0 | 750 | 0.6071 | 1.8293 |
| 1.4349 | 4.0 | 1000 | 0.6043 | 1.8974 |
| 1.3111 | 5.0 | 1250 | 0.6015 | 1.9769 |
| 1.197 | 6.0 | 1500 | 0.5992 | 2.0635 |
| 1.0729 | 7.0 | 1750 | 0.5975 | 2.1523 |
| 0.9833 | 8.0 | 2000 | 0.5947 | 2.2640 |
| 0.8672 | 9.0 | 2250 | 0.5924 | 2.3643 |
| 0.7883 | 10.0 | 2500 | 0.5908 | 2.4598 |
| 0.6879 | 11.0 | 2750 | 0.5890 | 2.5669 |
| 0.6295 | 12.0 | 3000 | 0.5885 | 2.7000 |
| 0.5545 | 13.0 | 3250 | 0.5851 | 2.8281 |
| 0.5208 | 14.0 | 3500 | 0.5853 | 2.8794 |
| 0.4679 | 15.0 | 3750 | 0.5863 | 2.9184 |
| 0.4464 | 16.0 | 4000 | 0.5852 | 3.0791 |
| 0.4136 | 17.0 | 4250 | 0.5856 | 3.0832 |
| 0.4021 | 18.0 | 4500 | 0.5847 | 3.0944 |
| 0.3776 | 19.0 | 4750 | 0.5828 | 3.2120 |
| 0.373 | 20.0 | 5000 | 0.5839 | 3.2298 |
| 0.3572 | 21.0 | 5250 | 0.5841 | 3.2434 |
| 0.3517 | 22.0 | 5500 | 0.5847 | 3.2606 |
| 0.3374 | 23.0 | 5750 | 0.5845 | 3.3392 |
| 0.3338 | 24.0 | 6000 | 0.5841 | 3.3489 |
| 0.3286 | 25.0 | 6250 | 0.5846 | 3.4036 |
| 0.3259 | 26.0 | 6500 | 0.5849 | 3.3878 |
| 0.3175 | 27.0 | 6750 | 0.5853 | 3.4960 |
| 0.3185 | 28.0 | 7000 | 0.5852 | 3.4873 |
| 0.3117 | 29.0 | 7250 | 0.5840 | 3.4780 |
| 0.3125 | 30.0 | 7500 | 0.5836 | 3.5383 |
| 0.3041 | 31.0 | 7750 | 0.5841 | 3.5253 |
| 0.3047 | 32.0 | 8000 | 0.5853 | 3.5283 |
| 0.2982 | 33.0 | 8250 | 0.5833 | 3.5511 |
| 0.3013 | 34.0 | 8500 | 0.5852 | 3.5445 |
| 0.295 | 35.0 | 8750 | 0.5841 | 3.5891 |
| 0.2988 | 36.0 | 9000 | 0.5833 | 3.6198 |
| 0.2939 | 37.0 | 9250 | 0.5842 | 3.5708 |
| 0.2952 | 38.0 | 9500 | 0.5833 | 3.6124 |
| 0.2927 | 39.0 | 9750 | 0.5840 | 3.6413 |
| 0.2931 | 40.0 | 10000 | 0.5828 | 3.6555 |
| 0.2891 | 41.0 | 10250 | 0.5841 | 3.6471 |
| 0.291 | 42.0 | 10500 | 0.5846 | 3.7233 |
| 0.2886 | 43.0 | 10750 | 0.5850 | 3.6348 |
| 0.289 | 44.0 | 11000 | 0.5839 | 3.6786 |
| 0.2846 | 45.0 | 11250 | 0.5845 | 3.6846 |
| 0.2858 | 46.0 | 11500 | 0.5855 | 3.7088 |
| 0.283 | 47.0 | 11750 | 0.5842 | 3.6938 |
| 0.2863 | 48.0 | 12000 | 0.5830 | 3.6793 |
| 0.2782 | 49.0 | 12250 | 0.5839 | 3.6805 |
| 0.2834 | 50.0 | 12500 | 0.5849 | 3.6692 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1