Hugging Face's logo

khangmacon
/
cyllama3

PEFT
Safetensors
Generated from Trainer
Eval Results
Model card Files Community
cyllama3 / README.md
khangmacon's picture
khangmacon
End of training
b665019 verified
preview code
|
raw
history blame contribute delete
7.84 kB
metadata
license: llama3
library_name: peft
tags:
  - generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
  - khangmacon/llmtrain
metrics:
  - accuracy
model-index:
  - name: cyllama3
    results:
      - task:
          type: text-generation
          name: Causal Language Modeling
        dataset:
          name: khangmacon/llmtrain
          type: khangmacon/llmtrain
        metrics:
          - type: accuracy
            value: 0.5590444975644216
            name: Accuracy

cyllama3

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the khangmacon/llmtrain dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9930
  • Accuracy: 0.5590

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2432 0.01 500 2.1239 0.5358
2.209 0.02 1000 2.0922 0.5404
2.1988 0.03 1500 2.0742 0.5436
2.1877 0.04 2000 2.0615 0.5463
2.1743 0.05 2500 2.0514 0.5479
2.1885 0.06 3000 2.0427 0.5495
2.1883 0.07 3500 2.0355 0.5509
2.1954 0.08 4000 2.0298 0.5519
2.1597 0.09 4500 2.0254 0.5526
2.1763 0.1 5000 2.0222 0.5532
2.1413 0.11 5500 2.0195 0.5541
2.1812 0.12 6000 2.0169 0.5545
2.1526 0.14 6500 2.0148 0.5547
2.155 0.15 7000 2.0131 0.5554
2.1594 0.16 7500 2.0110 0.5558
2.1681 0.17 8000 2.0097 0.5559
2.1572 0.18 8500 2.0083 0.5562
2.0943 0.19 9000 2.0074 0.5566
2.1421 0.2 9500 2.0063 0.5566
2.1196 0.21 10000 2.0049 0.5568
2.1634 0.22 10500 2.0042 0.5568
2.1361 0.23 11000 2.0035 0.5573
2.1614 0.24 11500 2.0027 0.5572
2.1205 0.25 12000 2.0021 0.5576
2.0984 0.26 12500 2.0011 0.5576
2.1226 0.27 13000 2.0006 0.5575
2.1054 0.28 13500 2.0001 0.5577
2.1297 0.29 14000 1.9997 0.5578
2.1233 0.3 14500 1.9988 0.5581
2.1348 0.31 15000 1.9984 0.5581
2.1494 0.32 15500 1.9980 0.5582
2.0827 0.33 16000 1.9976 0.5584
2.0991 0.34 16500 1.9975 0.5582
2.1108 0.35 17000 1.9972 0.5582
2.1209 0.36 17500 1.9968 0.5583
2.1012 0.37 18000 1.9963 0.5584
2.1155 0.38 18500 1.9959 0.5585
2.1493 0.4 19000 1.9956 0.5585
2.1219 0.41 19500 1.9953 0.5587
2.1584 0.42 20000 1.9952 0.5588
2.1167 0.43 20500 1.9950 0.5587
2.1507 0.44 21000 1.9948 0.5586
2.1043 0.45 21500 1.9946 0.5587
2.0864 0.46 22000 1.9945 0.5587
2.1074 0.47 22500 1.9943 0.5587
2.0858 0.48 23000 1.9942 0.5590
2.1178 0.49 23500 1.9941 0.5588
2.1148 0.5 24000 1.9940 0.5588
2.1165 0.51 24500 1.9939 0.5588
2.1012 0.52 25000 1.9938 0.5590
2.1573 0.53 25500 1.9936 0.5590
2.1674 0.54 26000 1.9936 0.5589
2.1184 0.55 26500 1.9935 0.5590
2.1424 0.56 27000 1.9935 0.5590
2.1437 0.57 27500 1.9935 0.5590
2.1244 0.58 28000 1.9933 0.5591
2.0767 0.59 28500 1.9933 0.5589
2.1182 0.6 29000 1.9934 0.5591
2.1277 0.61 29500 1.9933 0.5591
2.1407 0.62 30000 1.9932 0.5591
2.1222 0.63 30500 1.9932 0.5591
2.1146 0.64 31000 1.9931 0.5591
2.1441 0.65 31500 1.9932 0.5591
2.1224 0.67 32000 1.9931 0.5590
2.0878 0.68 32500 1.9932 0.5591
2.1172 0.69 33000 1.9932 0.5590
2.1166 0.7 33500 1.9931 0.5592
2.1054 0.71 34000 1.9931 0.5591
2.0972 0.72 34500 1.9931 0.5590
2.1228 0.73 35000 1.9931 0.5590
2.1231 0.74 35500 1.9931 0.5592
2.0974 0.75 36000 1.9931 0.5590
2.1025 0.76 36500 1.9931 0.5591
2.1217 0.77 37000 1.9931 0.5590
2.1227 0.78 37500 1.9930 0.5591
2.1272 0.79 38000 1.9931 0.5592
2.117 0.8 38500 1.9931 0.5591
2.1325 0.81 39000 1.9931 0.5591
2.1046 0.82 39500 1.9930 0.5591
2.1096 0.83 40000 1.9930 0.5591
2.1149 0.84 40500 1.9931 0.5591
2.122 0.85 41000 1.9931 0.5591
2.1137 0.86 41500 1.9931 0.5591
2.0983 0.87 42000 1.9930 0.5590
2.1109 0.88 42500 1.9931 0.5591
2.172 0.89 43000 1.9930 0.5590
2.0882 0.9 43500 1.9930 0.5591
2.0646 0.91 44000 1.9930 0.5591
2.1223 0.93 44500 1.9930 0.5591
2.1342 0.94 45000 1.9930 0.5591
2.0991 0.95 45500 1.9930 0.5590
2.1431 0.96 46000 1.9930 0.5592
2.0965 0.97 46500 1.9931 0.5590
2.1377 0.98 47000 1.9931 0.5592
2.1118 0.99 47500 1.9931 0.5592
2.089 1.0 48000 1.9930 0.5590

Framework versions

  • PEFT 0.10.1.dev0
  • Transformers 4.39.3
  • Pytorch 2.2.0
  • Datasets 2.18.0
  • Tokenizers 0.15.2