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RichardErkhov/CharlesLi_-_OpenELM-1_1B-DPO-full-max-14-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-14-reward - GGUF

  • —Model creator: https://huggingface.co/CharlesLi/
  • —Original model: https://huggingface.co/CharlesLi/OpenELM-1_1B-DPO-full-max-14-reward/

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

  • —trl
  • —dpo
  • —alignment-handbook
  • —generatedfromtrainer model-index:
  • —name: OpenELM-1_1B-DPO-full-max-14-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-14-reward

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

  • —Loss: 1.1668
  • —Rewards/chosen: -3.5938
  • —Rewards/rejected: -4.0
  • —Rewards/accuracies: 0.4902
  • —Rewards/margins: 0.4121
  • —Logps/rejected: -688.0
  • —Logps/chosen: -676.0
  • —Logits/rejected: -16.375
  • —Logits/chosen: -16.875

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
0.05620.10471000.6971-1.2578-1.57030.57620.3145-446.0-444.0-9.3125-9.5625
0.03940.20942000.7479-0.8516-1.00780.51950.1572-390.0-404.0-12.3125-12.75
0.04870.31413000.9195-1.9922-2.31250.51760.3203-520.0-516.0-13.4375-13.6875
0.04540.41884000.8309-1.4453-1.60160.49610.1543-448.0-462.0-15.625-15.75
0.02970.52365000.8326-3.1094-3.3750.50390.2734-628.0-628.0-15.5-15.6875
0.04340.62836000.8373-1.6953-1.8750.49410.1826-476.0-488.0-15.0-15.25
0.04960.73307000.9407-3.7344-3.96880.53320.2236-684.0-692.0-9.5625-10.3125
0.02890.83778001.0108-3.1406-3.250.47070.0991-612.0-632.0-13.0625-13.3125
0.02590.94249001.0869-3.6094-3.78120.46480.1631-668.0-680.0-15.625-15.875
0.0051.047110001.0944-3.4375-3.6250.45700.1758-652.0-664.0-15.0625-15.25
0.01561.151811001.2452-4.4062-4.59380.46290.1973-748.0-760.0-16.5-16.625
0.00181.256512001.0496-3.7344-3.92190.48440.1885-680.0-692.0-15.5625-15.875
0.00461.361313001.0484-3.375-3.60940.49800.2402-648.0-656.0-14.9375-15.25
0.00411.466014000.9980-3.5156-3.84380.51370.3379-676.0-668.0-13.8125-14.3125
0.00771.570715001.0434-3.1719-3.51560.49020.3535-640.0-636.0-13.875-14.375
0.00161.675416001.0882-3.8594-4.28120.49220.4141-716.0-704.0-12.4375-12.9375
0.00421.780117001.0261-3.3438-3.76560.49410.4238-664.0-652.0-15.5-15.9375
0.00051.884818001.0536-3.2344-3.59380.49610.3555-648.0-644.0-16.625-17.0
0.00831.989519001.1039-3.4844-3.81250.48830.3242-672.0-668.0-16.25-16.625
0.00032.094220001.1159-3.5156-3.84380.49220.3301-672.0-672.0-16.125-16.625
0.00272.199021001.1535-3.5938-4.00.49800.4043-688.0-680.0-16.125-16.625
0.00032.303722001.1505-3.5781-3.98440.49020.4062-688.0-676.0-16.25-16.625
0.00062.408423001.1535-3.5469-3.95310.49020.4023-684.0-672.0-16.25-16.75
0.00022.513124001.1581-3.5781-3.98440.49220.4082-688.0-676.0-16.25-16.625
0.00012.617825001.1609-3.5625-3.96880.49610.4082-684.0-672.0-16.375-16.75
0.00082.722526001.1668-3.5938-4.00.49220.4121-688.0-676.0-16.375-16.75
0.00022.827227001.1668-3.5938-4.00.49020.4121-688.0-676.0-16.375-16.75
0.00032.931928001.1668-3.5938-4.00.49020.4121-688.0-676.0-16.375-16.875

Framework versions

  • —Transformers 4.45.1
  • —Pytorch 2.3.0
  • —Datasets 3.0.1
  • —Tokenizers 0.20.0