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RichardErkhov/tsavage68_-_IE_M2_350steps_1e8rate_01beta_cSFTDPO-gguf

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

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IEM2350steps1e8rate01beta_cSFTDPO - GGUF

  • —Model creator: https://huggingface.co/tsavage68/
  • —Original model: https://huggingface.co/tsavage68/IEM2350steps1e8rate01beta_cSFTDPO/

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

  • —trl
  • —dpo
  • —generatedfromtrainer model-index:
  • —name: IEM2350steps1e8rate01beta_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. -->

IEM2350steps1e8rate01beta_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: 0.6859
  • —Rewards/chosen: -0.0025
  • —Rewards/rejected: -0.0173
  • —Rewards/accuracies: 0.3900
  • —Rewards/margins: 0.0148
  • —Logps/rejected: -41.1945
  • —Logps/chosen: -42.2302
  • —Logits/rejected: -2.9157
  • —Logits/chosen: -2.8544

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
0.69340.4500.69350.00120.00180.2150-0.0007-41.0035-42.1940-2.9159-2.8546
0.69290.81000.6915-0.0004-0.00370.27000.0033-41.0589-42.2096-2.9161-2.8547
0.68861.21500.6887-0.0011-0.01010.34500.0091-41.1233-42.2161-2.9157-2.8544
0.68871.62000.68630.0011-0.01280.38000.0139-41.1500-42.1946-2.9154-2.8540
0.68862.02500.6863-0.0008-0.01460.38500.0138-41.1676-42.2131-2.9157-2.8544
0.68932.43000.6857-0.0018-0.01700.39000.0152-41.1921-42.2240-2.9157-2.8544
0.68512.83500.6859-0.0025-0.01730.39000.0148-41.1945-42.2302-2.9157-2.8544

Framework versions

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