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---
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_docidx_v3_5e-4_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.19446153846153846
---
<!-- 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_docidx_v3_5e-4_lora2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 8.0353
- Accuracy: 0.1945
## 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: 0.0005
- 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 |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.3903 | 1.0 | 341 | 0.4564 | 3.9959 |
| 1.2 | 2.0 | 683 | 0.4396 | 4.5103 |
| 0.9155 | 3.0 | 1024 | 0.4285 | 4.8751 |
| 0.6446 | 4.0 | 1366 | 0.4326 | 4.8178 |
| 0.455 | 5.0 | 1707 | 0.4404 | 4.9434 |
| 0.3104 | 6.0 | 2049 | 0.4313 | 5.2226 |
| 0.2187 | 7.0 | 2390 | 0.4296 | 5.1633 |
| 0.1903 | 8.0 | 2732 | 0.4377 | 5.0743 |
| 0.1639 | 9.0 | 3073 | 0.4339 | 5.2491 |
| 0.1685 | 10.0 | 3415 | 0.4356 | 5.1677 |
| 0.1575 | 11.0 | 3756 | 0.4358 | 5.0421 |
| 0.151 | 12.0 | 4098 | 0.4338 | 5.1801 |
| 0.1586 | 13.0 | 4439 | 0.4347 | 5.2149 |
| 0.1492 | 14.0 | 4781 | 0.4356 | 5.1413 |
| 0.1539 | 15.0 | 5122 | 0.4309 | 5.2818 |
| 0.1472 | 16.0 | 5464 | 0.4372 | 5.0858 |
| 0.1503 | 17.0 | 5805 | 0.4341 | 5.1719 |
| 0.1449 | 18.0 | 6147 | 0.4301 | 5.3105 |
| 0.1384 | 19.0 | 6488 | 0.4263 | 5.2427 |
| 0.1472 | 20.0 | 6830 | 0.4309 | 5.2501 |
| 0.1389 | 21.0 | 7171 | 0.4309 | 5.0945 |
| 0.1456 | 22.0 | 7513 | 0.4327 | 5.2462 |
| 0.1398 | 23.0 | 7854 | 0.428 | 5.4476 |
| 0.1342 | 24.0 | 8196 | 0.4322 | 5.2605 |
| 0.1414 | 25.0 | 8537 | 0.4284 | 5.3590 |
| 0.1364 | 26.0 | 8879 | 0.4277 | 5.4423 |
| 0.1427 | 27.0 | 9220 | 0.4242 | 5.5243 |
| 0.1351 | 28.0 | 9562 | 0.4295 | 5.4508 |
| 0.1412 | 29.0 | 9903 | 0.4302 | 5.3767 |
| 0.1369 | 30.0 | 10245 | 0.4257 | 5.4378 |
| 0.1332 | 31.0 | 10586 | 0.4288 | 5.5004 |
| 0.14 | 32.0 | 10928 | 0.4261 | 5.6715 |
| 0.1336 | 33.0 | 11269 | 0.4268 | 5.5130 |
| 0.1412 | 34.0 | 11611 | 0.4266 | 5.5420 |
| 0.1357 | 35.0 | 11952 | 0.4182 | 5.6517 |
| 0.1363 | 36.0 | 12294 | 0.4208 | 5.4598 |
| 0.134 | 37.0 | 12635 | 0.4221 | 5.6220 |
| 0.1255 | 38.0 | 12977 | 0.4227 | 5.6988 |
| 0.1303 | 39.0 | 13318 | 0.4252 | 5.5511 |
| 0.2073 | 40.0 | 13660 | 0.4109 | 5.6976 |
| 0.1609 | 41.0 | 14001 | 0.4095 | 5.6908 |
| 0.1384 | 42.0 | 14343 | 0.4166 | 5.7460 |
| 0.1401 | 43.0 | 14684 | 0.4145 | 5.6377 |
| 0.1535 | 44.0 | 15026 | 0.4209 | 5.5295 |
| 0.1542 | 45.0 | 15367 | 0.2157 | 7.6505 |
| 7.7307 | 46.0 | 15709 | 0.2470 | 6.9279 |
| 7.3843 | 47.0 | 16050 | 0.1716 | 8.9680 |
| 8.5059 | 48.0 | 16392 | 0.1716 | 8.8324 |
| 7.9257 | 49.0 | 16733 | 0.1924 | 7.8902 |
| 7.855 | 49.93 | 17050 | 0.1945 | 8.0353 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
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