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RichardErkhov/tsavage68_-_IE_M2_350steps_1e8rate_03beta_cSFTDPO-gguf

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

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IEM2350steps1e8rate03beta_cSFTDPO - GGUF

  • —Model creator: https://huggingface.co/tsavage68/
  • —Original model: https://huggingface.co/tsavage68/IEM2350steps1e8rate03beta_cSFTDPO/

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

  • —trl
  • —dpo
  • —generatedfromtrainer model-index:
  • —name: IEM2350steps1e8rate03beta_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. -->

IEM2350steps1e8rate03beta_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: 0.6746
  • —Rewards/chosen: -0.0013
  • —Rewards/rejected: -0.0404
  • —Rewards/accuracies: 0.3600
  • —Rewards/margins: 0.0391
  • —Logps/rejected: -41.1564
  • —Logps/chosen: -42.2098
  • —Logits/rejected: -2.9159
  • —Logits/chosen: -2.8545

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-08
  • —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: 350

Training results

Training LossEpochStepValidation LossRewards/chosenRewards/rejectedRewards/accuraciesRewards/marginsLogps/rejectedLogps/chosenLogits/rejectedLogits/chosen
0.69980.4500.69490.00580.00850.2050-0.0028-40.9934-42.1863-2.9160-2.8547
0.69250.81000.69060.0017-0.00410.26000.0059-41.0355-42.1997-2.9159-2.8546
0.6791.21500.67790.0047-0.02730.36500.0320-41.1127-42.1899-2.9158-2.8546
0.67151.62000.67470.0020-0.03670.39000.0387-41.1442-42.1988-2.9156-2.8544
0.67642.02500.6736-0.0012-0.04190.38500.0407-41.1614-42.2094-2.9156-2.8543
0.68422.43000.6763-0.0024-0.03800.35000.0355-41.1483-42.2137-2.9159-2.8545
0.67122.83500.6746-0.0013-0.04040.36000.0391-41.1564-42.2098-2.9159-2.8545

Framework versions

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