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RichardErkhov/CharlesLi_-_OpenELM-1_1B-DPO-full-max-second-reward-gguf

sourceHugging Faceupdated 1y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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OpenELM-1_1B-DPO-full-max-second-reward - GGUF

  • —Model creator: https://huggingface.co/CharlesLi/
  • —Original model: https://huggingface.co/CharlesLi/OpenELM-1_1B-DPO-full-max-second-reward/

Original model description: --- library_name: transformers tags:

  • —trl
  • —dpo
  • —alignment-handbook
  • —generatedfromtrainer model-index:
  • —name: OpenELM-1_1B-DPO-full-max-second-reward results: [] ---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

OpenELM-1_1B-DPO-full-max-second-reward

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.4829
  • —Rewards/chosen: -12.4375
  • —Rewards/rejected: -12.875
  • —Rewards/accuracies: 0.5371
  • —Rewards/margins: 0.4414
  • —Logps/rejected: -1576.0
  • —Logps/chosen: -1560.0
  • —Logits/rejected: 10.8125
  • —Logits/chosen: 8.8125

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: 5e-05
  • —trainbatchsize: 8
  • —evalbatchsize: 16
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 4
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 64
  • —totalevalbatch_size: 64
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_ratio: 0.1
  • —num_epochs: 3

Training results

Training LossEpochStepValidation LossRewards/chosenRewards/rejectedRewards/accuraciesRewards/marginsLogps/rejectedLogps/chosenLogits/rejectedLogits/chosen
0.69270.10471000.6950-0.2334-0.25390.52540.0201-314.0-342.0-13.125-13.25
0.67590.20942000.7065-0.6484-0.76170.54880.1123-366.0-384.0-11.6875-11.9375
0.69120.31413000.7235-1.1484-1.23440.55270.0845-412.0-434.0-14.0-14.0625
0.70020.41884000.7412-1.2734-1.25780.4883-0.0128-414.0-446.0-13.5-13.5
0.68190.52365000.7542-1.75-1.76560.49610.0173-466.0-492.0-12.125-12.3125
0.70650.62836000.7290-1.9297-1.94530.50390.0159-482.0-512.0-12.1875-12.375
0.68920.73307000.7298-2.1094-2.17190.51170.0518-506.0-532.0-11.75-11.8125
0.71170.83778000.7436-2.25-2.28120.49610.0247-516.0-544.0-8.5625-8.875
0.68350.94249000.7565-2.1562-2.18750.51370.0284-508.0-536.0-7.8125-8.1875
0.27751.047110000.9428-4.0938-4.1250.51370.0229-700.0-728.0-10.75-11.1875
0.24711.151811000.9772-5.6562-5.750.52340.0986-864.0-884.0-3.9844-4.8438
0.24651.256512000.9777-5.125-5.21880.52540.0688-808.0-832.0-4.1562-5.0312
0.26011.361313000.9855-6.5-6.68750.54880.1846-956.0-968.00.3164-0.7695
0.24041.466014000.9077-6.8438-7.09380.52930.2520-1000.0-1004.02.03120.6367
0.23711.570715000.9027-5.8438-6.06250.55080.2061-896.0-904.01.41410.0143
0.23291.675416000.9480-6.7812-7.03120.54880.2617-992.0-996.02.03120.5664
0.2311.780117000.8705-6.2812-6.56250.55270.2598-944.0-948.0-1.6484-2.7031
0.20451.884818000.9315-7.4375-7.71880.56250.3086-1064.0-1064.0-1.3906-2.5
0.24671.989519000.8831-7.0625-7.3750.55860.3145-1024.0-1024.00.2656-0.9961
0.03772.094220001.3504-10.6875-11.06250.53710.3652-1392.0-1384.06.254.5625
0.02652.199021001.5050-11.5-11.81250.55660.3320-1472.0-1472.08.18756.375
0.03632.303722001.4563-11.625-11.93750.53120.3398-1480.0-1480.08.93757.1562
0.02922.408423001.5373-12.125-12.50.54490.3535-1536.0-1528.09.68757.7812
0.04912.513124001.4556-12.0625-12.50.54100.4355-1536.0-1528.09.81257.875
0.03242.617825001.4875-12.5-12.93750.53910.4414-1584.0-1568.010.58.5625
0.02472.722526001.4541-12.0625-12.50.54100.4336-1536.0-1528.010.258.3125
0.03352.827227001.4734-12.3125-12.750.53710.4434-1568.0-1552.010.68758.75
0.02632.931928001.4829-12.4375-12.8750.53710.4414-1576.0-1560.010.81258.8125

Framework versions

  • —Transformers 4.44.2
  • —Pytorch 2.3.0
  • —Datasets 3.0.0
  • —Tokenizers 0.19.1