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Adding Evaluation Results (#1)
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metadata
license: apache-2.0
library_name: peft
tags:
  - alignment-handbook
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
base_model: mistralai/Mistral-7B-v0.1
model-index:
  - name: r-zephyr-7b-beta-qlora
    results: []

r-zephyr-7b-beta-qlora

The 'r' means replicate. This model is a model replicated by using https://github.com/huggingface/alignment-handbook.

This model is a fine-tuned version on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5232
  • Rewards/chosen: -0.9374
  • Rewards/rejected: -1.7181
  • Rewards/accuracies: 0.7734
  • Rewards/margins: 0.7807
  • Logps/rejected: -420.1122
  • Logps/chosen: -341.2448
  • Logits/rejected: 0.6190
  • Logits/chosen: 0.6345

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: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • 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

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.5917 0.21 100 0.5950 -0.3904 -0.7775 0.7109 0.3872 -326.0618 -286.5451 -1.9790 -1.9769
0.5281 0.42 200 0.5492 -0.8657 -1.6137 0.7617 0.7479 -409.6739 -334.0814 -0.2289 -0.2367
0.5321 0.63 300 0.5321 -0.7444 -1.4427 0.7734 0.6983 -392.5731 -321.9463 0.3829 0.3741
0.5149 0.84 400 0.5233 -0.9570 -1.7432 0.7617 0.7862 -422.6298 -343.2071 0.6479 0.6688

Framework versions

  • PEFT 0.7.1
  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.2

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 62.70
AI2 Reasoning Challenge (25-Shot) 63.05
HellaSwag (10-Shot) 85.38
MMLU (5-Shot) 63.10
TruthfulQA (0-shot) 46.32
Winogrande (5-shot) 79.32
GSM8k (5-shot) 39.04