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mistral-7b-expo-7b-L2EXPO-25-8

PEFT
Safetensors
mistral
alignment-handbook
ndcg
trl
expo
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metadata
base_model: hZzy/mistral-7b-sft-25-1
datasets:
  - hZzy/direction_right2
library_name: peft
license: apache-2.0
tags:
  - alignment-handbook
  - ndcg
  - trl
  - expo
  - generated_from_trainer
model-index:
  - name: mistral-7b-expo-7b-L2EXPO-25-8
    results: []

Visualize in Weights & Biases

mistral-7b-expo-7b-L2EXPO-25-8

This model is a fine-tuned version of hZzy/mistral-7b-sft-25-1 on the hZzy/direction_right2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4420
  • Objective: 0.4416
  • Reward Accuracy: 0.6655
  • Logp Accuracy: 0.6499
  • Log Diff Policy: 15.7908
  • Chosen Logps: -168.1699
  • Rejected Logps: -183.9607
  • Chosen Rewards: -0.7349
  • Rejected Rewards: -0.8890
  • Logits: -2.0174

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: 5e-06
  • train_batch_size: 3
  • eval_batch_size: 3
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • gradient_accumulation_steps: 12
  • total_train_batch_size: 108
  • total_eval_batch_size: 9
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Objective Reward Accuracy Logp Accuracy Log Diff Policy Chosen Logps Rejected Logps Chosen Rewards Rejected Rewards Logits
0.6077 0.1517 100 0.5074 0.5043 0.5702 0.5254 0.9522 -93.2911 -94.2433 0.0139 0.0082 -2.2027
0.5413 0.3033 200 0.4873 0.4899 0.6037 0.5937 8.2778 -125.3062 -133.5840 -0.3062 -0.3852 -1.9290
0.5306 0.4550 300 0.4690 0.4694 0.6373 0.6267 12.8023 -128.2087 -141.0110 -0.3353 -0.4595 -1.9229
0.4809 0.6067 400 0.4566 0.4556 0.6507 0.6390 13.4130 -110.8082 -124.2212 -0.1613 -0.2916 -2.1010
0.5005 0.7583 500 0.4521 0.4527 0.6566 0.6538 16.5186 -141.8083 -158.3269 -0.4713 -0.6326 -2.0970
0.4543 0.9100 600 0.4501 0.4510 0.6692 0.6586 17.7658 -179.5024 -197.2682 -0.8482 -1.0220 -2.0605

Framework versions

  • PEFT 0.11.1
  • Transformers 4.42.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.0
  • Tokenizers 0.19.1