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RichardErkhov/tsavage68_-_Na_M2_100steps_1e7rate_03beta_cSFTDPO-gguf

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

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NaM2100steps1e7rate03beta_cSFTDPO - GGUF

  • —Model creator: https://huggingface.co/tsavage68/
  • —Original model: https://huggingface.co/tsavage68/NaM2100steps1e7rate03beta_cSFTDPO/

Original model description: --- libraryname: transformers license: apache-2.0 basemodel: tsavage68/NaM21000steps1e7SFT tags:

  • —trl
  • —dpo
  • —generatedfromtrainer model-index:
  • —name: NaM2100steps1e7rate03beta_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. -->

NaM2100steps1e7rate03beta_cSFTDPO

This model is a fine-tuned version of tsavage68/Na_M2_1000steps_1e7_SFT on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.0000
  • —Rewards/chosen: 2.8383
  • —Rewards/rejected: -9.2541
  • —Rewards/accuracies: 1.0
  • —Rewards/margins: 12.0924
  • —Logps/rejected: -110.7703
  • —Logps/chosen: -38.6713
  • —Logits/rejected: -2.5103
  • —Logits/chosen: -2.5250

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-07
  • —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: 100

Training results

Training LossEpochStepValidation LossRewards/chosenRewards/rejectedRewards/accuraciesRewards/marginsLogps/rejectedLogps/chosenLogits/rejectedLogits/chosen
0.00040.2667500.00002.3360-8.06851.010.4045-106.8185-40.3458-2.5169-2.5309
0.00.53331000.00002.8383-9.25411.012.0924-110.7703-38.6713-2.5103-2.5250

Framework versions

  • —Transformers 4.44.2
  • —Pytorch 2.4.0+cu121
  • —Datasets 2.21.0
  • —Tokenizers 0.19.1