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--- |
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license: llama3 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: meta-llama/Meta-Llama-3-8B |
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datasets: |
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- khangmacon/llmtrain |
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metrics: |
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- accuracy |
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model-index: |
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- name: cyllama3 |
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results: |
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- task: |
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type: text-generation |
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name: Causal Language Modeling |
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dataset: |
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name: khangmacon/llmtrain |
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type: khangmacon/llmtrain |
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metrics: |
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- type: accuracy |
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value: 0.5590444975644216 |
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name: Accuracy |
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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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# cyllama3 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the khangmacon/llmtrain dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9930 |
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- Accuracy: 0.5590 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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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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| 2.2432 | 0.01 | 500 | 2.1239 | 0.5358 | |
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| 2.209 | 0.02 | 1000 | 2.0922 | 0.5404 | |
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| 2.1988 | 0.03 | 1500 | 2.0742 | 0.5436 | |
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| 2.1877 | 0.04 | 2000 | 2.0615 | 0.5463 | |
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| 2.1743 | 0.05 | 2500 | 2.0514 | 0.5479 | |
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| 2.1885 | 0.06 | 3000 | 2.0427 | 0.5495 | |
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| 2.1883 | 0.07 | 3500 | 2.0355 | 0.5509 | |
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| 2.1954 | 0.08 | 4000 | 2.0298 | 0.5519 | |
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| 2.1597 | 0.09 | 4500 | 2.0254 | 0.5526 | |
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| 2.1763 | 0.1 | 5000 | 2.0222 | 0.5532 | |
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| 2.1413 | 0.11 | 5500 | 2.0195 | 0.5541 | |
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| 2.1812 | 0.12 | 6000 | 2.0169 | 0.5545 | |
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| 2.1526 | 0.14 | 6500 | 2.0148 | 0.5547 | |
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| 2.155 | 0.15 | 7000 | 2.0131 | 0.5554 | |
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| 2.1594 | 0.16 | 7500 | 2.0110 | 0.5558 | |
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| 2.1681 | 0.17 | 8000 | 2.0097 | 0.5559 | |
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| 2.1572 | 0.18 | 8500 | 2.0083 | 0.5562 | |
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| 2.0943 | 0.19 | 9000 | 2.0074 | 0.5566 | |
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| 2.1421 | 0.2 | 9500 | 2.0063 | 0.5566 | |
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| 2.1196 | 0.21 | 10000 | 2.0049 | 0.5568 | |
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| 2.1634 | 0.22 | 10500 | 2.0042 | 0.5568 | |
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| 2.1361 | 0.23 | 11000 | 2.0035 | 0.5573 | |
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| 2.1614 | 0.24 | 11500 | 2.0027 | 0.5572 | |
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| 2.1205 | 0.25 | 12000 | 2.0021 | 0.5576 | |
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| 2.0984 | 0.26 | 12500 | 2.0011 | 0.5576 | |
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| 2.1226 | 0.27 | 13000 | 2.0006 | 0.5575 | |
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| 2.1054 | 0.28 | 13500 | 2.0001 | 0.5577 | |
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| 2.1297 | 0.29 | 14000 | 1.9997 | 0.5578 | |
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| 2.1233 | 0.3 | 14500 | 1.9988 | 0.5581 | |
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| 2.1348 | 0.31 | 15000 | 1.9984 | 0.5581 | |
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| 2.1494 | 0.32 | 15500 | 1.9980 | 0.5582 | |
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| 2.0827 | 0.33 | 16000 | 1.9976 | 0.5584 | |
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| 2.0991 | 0.34 | 16500 | 1.9975 | 0.5582 | |
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| 2.1108 | 0.35 | 17000 | 1.9972 | 0.5582 | |
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| 2.1209 | 0.36 | 17500 | 1.9968 | 0.5583 | |
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| 2.1012 | 0.37 | 18000 | 1.9963 | 0.5584 | |
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| 2.1155 | 0.38 | 18500 | 1.9959 | 0.5585 | |
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| 2.1493 | 0.4 | 19000 | 1.9956 | 0.5585 | |
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| 2.1219 | 0.41 | 19500 | 1.9953 | 0.5587 | |
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| 2.1584 | 0.42 | 20000 | 1.9952 | 0.5588 | |
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| 2.1167 | 0.43 | 20500 | 1.9950 | 0.5587 | |
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| 2.1507 | 0.44 | 21000 | 1.9948 | 0.5586 | |
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| 2.1043 | 0.45 | 21500 | 1.9946 | 0.5587 | |
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| 2.0864 | 0.46 | 22000 | 1.9945 | 0.5587 | |
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| 2.1074 | 0.47 | 22500 | 1.9943 | 0.5587 | |
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| 2.0858 | 0.48 | 23000 | 1.9942 | 0.5590 | |
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| 2.1178 | 0.49 | 23500 | 1.9941 | 0.5588 | |
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| 2.1148 | 0.5 | 24000 | 1.9940 | 0.5588 | |
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| 2.1165 | 0.51 | 24500 | 1.9939 | 0.5588 | |
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| 2.1012 | 0.52 | 25000 | 1.9938 | 0.5590 | |
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| 2.1573 | 0.53 | 25500 | 1.9936 | 0.5590 | |
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| 2.1674 | 0.54 | 26000 | 1.9936 | 0.5589 | |
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| 2.1184 | 0.55 | 26500 | 1.9935 | 0.5590 | |
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| 2.1424 | 0.56 | 27000 | 1.9935 | 0.5590 | |
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| 2.1437 | 0.57 | 27500 | 1.9935 | 0.5590 | |
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| 2.1244 | 0.58 | 28000 | 1.9933 | 0.5591 | |
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| 2.0767 | 0.59 | 28500 | 1.9933 | 0.5589 | |
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| 2.1182 | 0.6 | 29000 | 1.9934 | 0.5591 | |
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| 2.1277 | 0.61 | 29500 | 1.9933 | 0.5591 | |
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| 2.1407 | 0.62 | 30000 | 1.9932 | 0.5591 | |
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| 2.1222 | 0.63 | 30500 | 1.9932 | 0.5591 | |
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| 2.1146 | 0.64 | 31000 | 1.9931 | 0.5591 | |
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| 2.1441 | 0.65 | 31500 | 1.9932 | 0.5591 | |
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| 2.1224 | 0.67 | 32000 | 1.9931 | 0.5590 | |
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| 2.0878 | 0.68 | 32500 | 1.9932 | 0.5591 | |
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| 2.1172 | 0.69 | 33000 | 1.9932 | 0.5590 | |
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| 2.1166 | 0.7 | 33500 | 1.9931 | 0.5592 | |
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| 2.1054 | 0.71 | 34000 | 1.9931 | 0.5591 | |
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| 2.0972 | 0.72 | 34500 | 1.9931 | 0.5590 | |
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| 2.1228 | 0.73 | 35000 | 1.9931 | 0.5590 | |
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| 2.1231 | 0.74 | 35500 | 1.9931 | 0.5592 | |
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| 2.0974 | 0.75 | 36000 | 1.9931 | 0.5590 | |
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| 2.1025 | 0.76 | 36500 | 1.9931 | 0.5591 | |
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| 2.1217 | 0.77 | 37000 | 1.9931 | 0.5590 | |
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| 2.1227 | 0.78 | 37500 | 1.9930 | 0.5591 | |
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| 2.1272 | 0.79 | 38000 | 1.9931 | 0.5592 | |
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| 2.117 | 0.8 | 38500 | 1.9931 | 0.5591 | |
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| 2.1325 | 0.81 | 39000 | 1.9931 | 0.5591 | |
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| 2.1046 | 0.82 | 39500 | 1.9930 | 0.5591 | |
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| 2.1096 | 0.83 | 40000 | 1.9930 | 0.5591 | |
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| 2.1149 | 0.84 | 40500 | 1.9931 | 0.5591 | |
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| 2.122 | 0.85 | 41000 | 1.9931 | 0.5591 | |
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| 2.1137 | 0.86 | 41500 | 1.9931 | 0.5591 | |
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| 2.0983 | 0.87 | 42000 | 1.9930 | 0.5590 | |
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| 2.1109 | 0.88 | 42500 | 1.9931 | 0.5591 | |
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| 2.172 | 0.89 | 43000 | 1.9930 | 0.5590 | |
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| 2.0882 | 0.9 | 43500 | 1.9930 | 0.5591 | |
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| 2.0646 | 0.91 | 44000 | 1.9930 | 0.5591 | |
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| 2.1223 | 0.93 | 44500 | 1.9930 | 0.5591 | |
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| 2.1342 | 0.94 | 45000 | 1.9930 | 0.5591 | |
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| 2.0991 | 0.95 | 45500 | 1.9930 | 0.5590 | |
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| 2.1431 | 0.96 | 46000 | 1.9930 | 0.5592 | |
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| 2.0965 | 0.97 | 46500 | 1.9931 | 0.5590 | |
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| 2.1377 | 0.98 | 47000 | 1.9931 | 0.5592 | |
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| 2.1118 | 0.99 | 47500 | 1.9931 | 0.5592 | |
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| 2.089 | 1.0 | 48000 | 1.9930 | 0.5590 | |
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### Framework versions |
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- PEFT 0.10.1.dev0 |
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- Transformers 4.39.3 |
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- Pytorch 2.2.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
