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RichardErkhov/tsavage68_-_IE_M2_1000steps_1e5rate_01beta_cSFTDPO-gguf

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

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IEM21000steps1e5rate01beta_cSFTDPO - GGUF

  • —Model creator: https://huggingface.co/tsavage68/
  • —Original model: https://huggingface.co/tsavage68/IEM21000steps1e5rate01beta_cSFTDPO/

Original model description: --- libraryname: transformers license: apache-2.0 basemodel: tsavage68/IEM21000steps1e7rateSFT tags:

  • —trl
  • —dpo
  • —generatedfromtrainer model-index:
  • —name: IEM21000steps1e5rate01beta_cSFTDPO 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. -->

IEM21000steps1e5rate01beta_cSFTDPO

This model is a fine-tuned version of tsavage68/IE_M2_1000steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.3743
  • —Rewards/chosen: -2.2787
  • —Rewards/rejected: -22.2937
  • —Rewards/accuracies: 0.4600
  • —Rewards/margins: 20.0150
  • —Logps/rejected: -263.9586
  • —Logps/chosen: -64.9924
  • —Logits/rejected: -2.7884
  • —Logits/chosen: -2.7491

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-05
  • —trainbatchsize: 2
  • —evalbatchsize: 1
  • —seed: 42
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 4
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 100
  • —training_steps: 1000

Training results

Training LossEpochStepValidation LossRewards/chosenRewards/rejectedRewards/accuraciesRewards/marginsLogps/rejectedLogps/chosenLogits/rejectedLogits/chosen
0.45050.4500.3743-2.1144-14.88830.460012.7739-189.9051-63.3497-2.7726-2.7468
0.38120.81000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.31191.21500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.36391.62000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.43322.02500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.39862.43000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.39862.83500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.45053.24000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.45053.64500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.43324.05000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.32924.45500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.36394.86000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.45055.26500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.45055.67000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.36396.07500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.24266.48000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.50256.88500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.31197.29000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.34667.69500.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491
0.38128.010000.3743-2.2787-22.29370.460020.0150-263.9586-64.9924-2.7884-2.7491

Framework versions

  • —Transformers 4.44.2
  • —Pytorch 2.0.0+cu117
  • —Datasets 3.0.0
  • —Tokenizers 0.19.1