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meta-llama/CodeLlama-7b-Instruct-hf-FaVe-rank32-10epochs
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metadata
license: llama2
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
base_model: meta-llama/CodeLlama-7b-Instruct-hf
model-index:
  - name: CodeLlama-7b-Instruct-hf-FaVe-rank32-10epochs
    results: []

CodeLlama-7b-Instruct-hf-FaVe-rank32-10epochs

This model is a fine-tuned version of meta-llama/CodeLlama-7b-Instruct-hf on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4773

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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss
No log 0.2685 10 2.1933
2.2814 0.5369 20 1.6913
2.2814 0.8054 30 1.1039
1.2593 1.0738 40 0.7832
1.2593 1.3423 50 0.6804
0.7223 1.6107 60 0.6281
0.7223 1.8792 70 0.5940
0.5977 2.1477 80 0.5714
0.5977 2.4161 90 0.5421
0.5218 2.6846 100 0.5222
0.5218 2.9530 110 0.5033
0.4401 3.2215 120 0.4987
0.4401 3.4899 130 0.4822
0.3746 3.7584 140 0.4546
0.3746 4.0268 150 0.4331
0.3208 4.2953 160 0.4625
0.3208 4.5638 170 0.4269
0.2951 4.8322 180 0.4339
0.2951 5.1007 190 0.4123
0.2516 5.3691 200 0.4562
0.2516 5.6376 210 0.4228
0.2191 5.9060 220 0.4243
0.2191 6.1745 230 0.4357
0.2121 6.4430 240 0.4404
0.2121 6.7114 250 0.4212
0.1739 6.9799 260 0.4348
0.1739 7.2483 270 0.4444
0.1617 7.5168 280 0.4342
0.1617 7.7852 290 0.4529
0.1453 8.0537 300 0.4525
0.1453 8.3221 310 0.4722
0.1387 8.5906 320 0.4712
0.1387 8.8591 330 0.4691
0.1214 9.1275 340 0.4686
0.1214 9.3960 350 0.4728
0.1226 9.6644 360 0.4769
0.1226 9.9329 370 0.4773

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1