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--- |
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base_model: TheBloke/Llama-2-7B-fp16 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: llama7b_rulm_small_1e_17_10_23 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# llama7b_rulm_small_1e_17_10_23 |
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This model is a fine-tuned version of [TheBloke/Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0955 |
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- Accuracy: 0.5405 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 14 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 336 |
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- total_eval_batch_size: 168 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 2.2009 | 0.01 | 1000 | 2.1993 | 0.5228 | |
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| 2.1902 | 0.02 | 2000 | 2.1843 | 0.5246 | |
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| 2.179 | 0.02 | 3000 | 2.1763 | 0.5261 | |
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| 2.1773 | 0.03 | 4000 | 2.1722 | 0.5270 | |
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| 2.1708 | 0.04 | 5000 | 2.1679 | 0.5274 | |
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| 2.1695 | 0.05 | 6000 | 2.1648 | 0.5279 | |
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| 2.1662 | 0.05 | 7000 | 2.1638 | 0.5284 | |
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| 2.1635 | 0.06 | 8000 | 2.1613 | 0.5285 | |
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| 2.1668 | 0.07 | 9000 | 2.1595 | 0.5289 | |
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| 2.1597 | 0.08 | 10000 | 2.1580 | 0.5294 | |
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| 2.1593 | 0.08 | 11000 | 2.1572 | 0.5294 | |
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| 2.1561 | 0.09 | 12000 | 2.1556 | 0.5296 | |
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| 2.1525 | 0.1 | 13000 | 2.1543 | 0.5298 | |
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| 2.1557 | 0.11 | 14000 | 2.1534 | 0.5297 | |
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| 2.1547 | 0.12 | 15000 | 2.1526 | 0.5299 | |
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| 2.1544 | 0.12 | 16000 | 2.1516 | 0.5303 | |
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| 2.1562 | 0.13 | 17000 | 2.1512 | 0.5304 | |
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| 2.1515 | 0.14 | 18000 | 2.1506 | 0.5303 | |
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| 2.1516 | 0.15 | 19000 | 2.1488 | 0.5307 | |
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| 2.1519 | 0.15 | 20000 | 2.1493 | 0.5305 | |
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| 2.1506 | 0.16 | 21000 | 2.1474 | 0.5311 | |
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| 2.1484 | 0.17 | 22000 | 2.1483 | 0.5310 | |
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| 2.1533 | 0.18 | 23000 | 2.1471 | 0.5312 | |
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| 2.1471 | 0.18 | 24000 | 2.1470 | 0.5310 | |
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| 2.1421 | 0.19 | 25000 | 2.1454 | 0.5313 | |
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| 2.1452 | 0.2 | 26000 | 2.1452 | 0.5314 | |
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| 2.1481 | 0.21 | 27000 | 2.1438 | 0.5317 | |
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| 2.149 | 0.22 | 28000 | 2.1441 | 0.5317 | |
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| 2.1483 | 0.22 | 29000 | 2.1435 | 0.5315 | |
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| 2.1453 | 0.23 | 30000 | 2.1428 | 0.5319 | |
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| 2.1442 | 0.24 | 31000 | 2.1425 | 0.5320 | |
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| 2.1411 | 0.25 | 32000 | 2.1413 | 0.5322 | |
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| 2.1418 | 0.25 | 33000 | 2.1409 | 0.5322 | |
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| 2.1394 | 0.26 | 34000 | 2.1409 | 0.5323 | |
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| 2.1415 | 0.27 | 35000 | 2.1403 | 0.5323 | |
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| 2.139 | 0.28 | 36000 | 2.1404 | 0.5321 | |
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| 2.1403 | 0.28 | 37000 | 2.1394 | 0.5324 | |
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| 2.1382 | 0.29 | 38000 | 2.1395 | 0.5325 | |
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| 2.1375 | 0.3 | 39000 | 2.1392 | 0.5323 | |
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| 2.1403 | 0.31 | 40000 | 2.1382 | 0.5328 | |
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| 2.1385 | 0.31 | 41000 | 2.1378 | 0.5328 | |
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| 2.1356 | 0.32 | 42000 | 2.1371 | 0.5328 | |
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| 2.1388 | 0.33 | 43000 | 2.1370 | 0.5330 | |
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| 2.1347 | 0.34 | 44000 | 2.1361 | 0.5329 | |
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| 2.1384 | 0.35 | 45000 | 2.1357 | 0.5332 | |
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| 2.1391 | 0.35 | 46000 | 2.1352 | 0.5333 | |
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| 2.1342 | 0.36 | 47000 | 2.1356 | 0.5330 | |
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| 2.1319 | 0.37 | 48000 | 2.1347 | 0.5334 | |
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| 2.1305 | 0.38 | 49000 | 2.1345 | 0.5334 | |
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| 2.1312 | 0.38 | 50000 | 2.1339 | 0.5334 | |
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| 2.1352 | 0.39 | 51000 | 2.1334 | 0.5336 | |
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| 2.1342 | 0.4 | 52000 | 2.1335 | 0.5339 | |
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| 2.1355 | 0.41 | 53000 | 2.1318 | 0.5339 | |
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| 2.1333 | 0.41 | 54000 | 2.1320 | 0.5340 | |
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| 2.1315 | 0.42 | 55000 | 2.1316 | 0.5338 | |
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| 2.1316 | 0.43 | 56000 | 2.1311 | 0.5340 | |
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| 2.1332 | 0.44 | 57000 | 2.1309 | 0.5339 | |
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| 2.1258 | 0.45 | 58000 | 2.1298 | 0.5341 | |
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| 2.1302 | 0.45 | 59000 | 2.1293 | 0.5345 | |
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| 2.1318 | 0.46 | 60000 | 2.1287 | 0.5345 | |
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| 2.1247 | 0.47 | 61000 | 2.1289 | 0.5342 | |
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| 2.1282 | 0.48 | 62000 | 2.1276 | 0.5345 | |
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| 2.1225 | 0.48 | 63000 | 2.1276 | 0.5346 | |
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| 2.1288 | 0.49 | 64000 | 2.1265 | 0.5344 | |
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| 2.1281 | 0.5 | 65000 | 2.1261 | 0.5346 | |
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| 2.1267 | 0.51 | 66000 | 2.1256 | 0.5348 | |
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| 2.1252 | 0.51 | 67000 | 2.1256 | 0.5349 | |
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| 2.1237 | 0.52 | 68000 | 2.1258 | 0.5349 | |
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| 2.1264 | 0.53 | 69000 | 2.1243 | 0.5353 | |
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| 2.1245 | 0.54 | 70000 | 2.1243 | 0.5352 | |
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| 2.1235 | 0.55 | 71000 | 2.1239 | 0.5352 | |
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| 2.1261 | 0.55 | 72000 | 2.1224 | 0.5357 | |
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| 2.1218 | 0.56 | 73000 | 2.1219 | 0.5355 | |
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| 2.1205 | 0.57 | 74000 | 2.1219 | 0.5356 | |
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| 2.1229 | 0.58 | 75000 | 2.1215 | 0.5355 | |
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| 2.1199 | 0.58 | 76000 | 2.1207 | 0.5358 | |
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| 2.1175 | 0.59 | 77000 | 2.1205 | 0.5358 | |
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| 2.1205 | 0.6 | 78000 | 2.1201 | 0.5359 | |
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| 2.1206 | 0.61 | 79000 | 2.1194 | 0.5362 | |
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| 2.1183 | 0.61 | 80000 | 2.1191 | 0.5361 | |
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| 2.1242 | 0.62 | 81000 | 2.1189 | 0.5361 | |
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| 2.1214 | 0.63 | 82000 | 2.1179 | 0.5361 | |
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| 2.1185 | 0.64 | 83000 | 2.1172 | 0.5362 | |
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| 2.1172 | 0.65 | 84000 | 2.1176 | 0.5362 | |
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| 2.1159 | 0.65 | 85000 | 2.1167 | 0.5367 | |
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| 2.1162 | 0.66 | 86000 | 2.1158 | 0.5367 | |
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| 2.1134 | 0.67 | 87000 | 2.1160 | 0.5367 | |
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| 2.1158 | 0.68 | 88000 | 2.1149 | 0.5369 | |
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| 2.1183 | 0.68 | 89000 | 2.1146 | 0.5371 | |
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| 2.1172 | 0.69 | 90000 | 2.1138 | 0.5371 | |
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| 2.1192 | 0.7 | 91000 | 2.1133 | 0.5370 | |
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| 2.1107 | 0.71 | 92000 | 2.1130 | 0.5372 | |
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| 2.1159 | 0.71 | 93000 | 2.1124 | 0.5375 | |
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| 2.113 | 0.72 | 94000 | 2.1120 | 0.5374 | |
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| 2.1151 | 0.73 | 95000 | 2.1113 | 0.5375 | |
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| 2.1117 | 0.74 | 96000 | 2.1107 | 0.5376 | |
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| 2.1111 | 0.75 | 97000 | 2.1104 | 0.5375 | |
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| 2.109 | 0.75 | 98000 | 2.1103 | 0.5378 | |
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| 2.1121 | 0.76 | 99000 | 2.1098 | 0.5379 | |
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| 2.1075 | 0.77 | 100000 | 2.1089 | 0.5377 | |
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| 2.1094 | 0.78 | 101000 | 2.1087 | 0.5378 | |
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| 2.1113 | 0.78 | 102000 | 2.1079 | 0.5381 | |
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| 2.1065 | 0.79 | 103000 | 2.1077 | 0.5380 | |
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| 2.107 | 0.8 | 104000 | 2.1071 | 0.5382 | |
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| 2.109 | 0.81 | 105000 | 2.1067 | 0.5385 | |
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| 2.1049 | 0.81 | 106000 | 2.1060 | 0.5384 | |
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| 2.1071 | 0.82 | 107000 | 2.1058 | 0.5386 | |
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| 2.1026 | 0.83 | 108000 | 2.1054 | 0.5385 | |
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| 2.1059 | 0.84 | 109000 | 2.1048 | 0.5388 | |
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| 2.1 | 0.85 | 110000 | 2.1043 | 0.5389 | |
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| 2.1017 | 0.85 | 111000 | 2.1038 | 0.5389 | |
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| 2.107 | 0.86 | 112000 | 2.1030 | 0.5390 | |
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| 2.101 | 0.87 | 113000 | 2.1028 | 0.5392 | |
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| 2.0995 | 0.88 | 114000 | 2.1023 | 0.5391 | |
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| 2.1076 | 0.88 | 115000 | 2.1018 | 0.5391 | |
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| 2.1011 | 0.89 | 116000 | 2.1012 | 0.5394 | |
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| 2.1006 | 0.9 | 117000 | 2.1008 | 0.5394 | |
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| 2.0955 | 0.91 | 118000 | 2.1004 | 0.5395 | |
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| 2.1007 | 0.91 | 119000 | 2.0999 | 0.5396 | |
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| 2.1022 | 0.92 | 120000 | 2.0995 | 0.5396 | |
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| 2.0978 | 0.93 | 121000 | 2.0990 | 0.5399 | |
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| 2.0981 | 0.94 | 122000 | 2.0984 | 0.5399 | |
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| 2.0952 | 0.94 | 123000 | 2.0980 | 0.5399 | |
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| 2.0962 | 0.95 | 124000 | 2.0974 | 0.5400 | |
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| 2.0993 | 0.96 | 125000 | 2.0971 | 0.5402 | |
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| 2.0982 | 0.97 | 126000 | 2.0967 | 0.5402 | |
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| 2.0962 | 0.98 | 127000 | 2.0964 | 0.5403 | |
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| 2.0963 | 0.98 | 128000 | 2.0960 | 0.5404 | |
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| 2.0967 | 0.99 | 129000 | 2.0958 | 0.5404 | |
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| 2.094 | 1.0 | 130000 | 2.0955 | 0.5405 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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