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metadata
library_name: transformers
license: other
base_model: trl-lib/qwen1.5-0.5b-sft
tags:
  - alignment-handbook
  - trl
  - simpo
  - generated_from_trainer
  - trl
  - simpo
  - generated_from_trainer
datasets:
  - yakazimir/ultrafeedback_binarized
model-index:
  - name: qwen_cfUNL_entropy_0_01
    results: []

qwen_cfUNL_entropy_0_01

This model is a fine-tuned version of trl-lib/qwen1.5-0.5b-sft on the yakazimir/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0501
  • Sft Loss: 3.9427
  • Rewards/chosen: -4.3435
  • Rewards/rejected: -5.1114
  • Rewards/accuracies: 0.6810
  • Rewards/margins: 0.7679
  • Logps/rejected: -5.1114
  • Logps/chosen: -4.3435
  • Logits/rejected: -0.0604
  • Logits/chosen: -0.1374

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: 1e-06
  • train_batch_size: 2
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss Sft Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.0548 0.2141 400 0.0564 4.2256 -4.8288 -5.0421 0.5378 0.2133 -5.0421 -4.8288 0.4440 0.3255
0.0531 0.4282 800 0.0526 4.0511 -4.5660 -4.9392 0.6157 0.3732 -4.9392 -4.5660 0.2541 0.1294
0.0534 0.6422 1200 0.0519 4.1663 -4.5650 -5.0390 0.6387 0.4740 -5.0390 -4.5650 0.2502 0.1373
0.0511 0.8563 1600 0.0513 3.9593 -4.4389 -4.9222 0.6358 0.4833 -4.9222 -4.4389 -0.0640 -0.1524
0.0533 1.0704 2000 0.0509 3.9533 -4.4316 -4.9577 0.6484 0.5261 -4.9577 -4.4316 -0.0257 -0.1111
0.0527 1.2845 2400 0.0508 4.2818 -4.7129 -5.3738 0.6610 0.6609 -5.3738 -4.7129 -0.0551 -0.1386
0.0513 1.4986 2800 0.0506 4.1502 -4.4933 -5.1357 0.6818 0.6424 -5.1357 -4.4933 -0.1729 -0.2577
0.0527 1.7127 3200 0.0505 4.1082 -4.4722 -5.1175 0.6743 0.6453 -5.1175 -4.4722 -0.0614 -0.1521
0.0538 1.9267 3600 0.0502 4.0026 -4.3928 -5.1056 0.6706 0.7129 -5.1056 -4.3928 -0.1185 -0.1939
0.0495 2.1408 4000 0.0502 4.0304 -4.4251 -5.1723 0.6825 0.7472 -5.1723 -4.4251 -0.0488 -0.1284
0.0522 2.3549 4400 0.0501 3.9711 -4.3751 -5.1111 0.6751 0.7360 -5.1111 -4.3751 -0.1449 -0.2170
0.0517 2.5690 4800 0.0501 4.0093 -4.3976 -5.1429 0.6832 0.7452 -5.1429 -4.3976 -0.0508 -0.1310
0.0496 2.7831 5200 0.0501 3.9605 -4.3494 -5.1084 0.6788 0.7590 -5.1084 -4.3494 -0.0700 -0.1475
0.0497 2.9972 5600 0.0501 3.9427 -4.3435 -5.1114 0.6810 0.7679 -5.1114 -4.3435 -0.0604 -0.1374

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1