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gemma-2-Ifable-9B - GGUF

Original model description:

license: gemma library_name: transformers datasets: - jondurbin/gutenberg-dpo-v0.1

ifable/gemma-2-Ifable-9B

This model ranked first on the Creative Writing Benchmark (https://eqbench.com/creative_writing.html) on September 10, 2024

Training and evaluation data

Training procedure

Training method: SimPO (GitHub - princeton-nlp/SimPO: SimPO: Simple Preference Optimization with a Reference-Free Reward)

It achieves the following results on the evaluation set:

  • Loss: 1.0163
  • Rewards/chosen: -21.6822
  • Rewards/rejected: -47.8754
  • Rewards/accuracies: 0.9167
  • Rewards/margins: 26.1931
  • Logps/rejected: -4.7875
  • Logps/chosen: -2.1682
  • Logits/rejected: -17.0475
  • Logits/chosen: -12.0041

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 8e-07
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen Sft Loss
1.4444 0.9807 35 1.0163 -21.6822 -47.8754 0.9167 26.1931 -4.7875 -2.1682 -17.0475 -12.0041 0.0184

Framework versions

  • Transformers 4.43.4
  • Pytorch 2.3.0a0+ebedce2
  • Datasets 2.20.0
  • Tokenizers 0.19.1

We are looking for product manager and operations managers to build applications through our model, and also open for business cooperation, and also AI engineer to join us, contact with : [email protected]

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