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pythia-1b-tulu-v2-mix-uf-rm

Text Classification
Transformers
Safetensors
gpt_neox
trl
reward-trainer
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
license: apache-2.0
base_model: kykim0/pythia-1b-tulu-v2-mix-nos
tags:
  - trl
  - reward-trainer
  - generated_from_trainer
datasets:
  - allenai/ultrafeedback_binarized_cleaned
metrics:
  - accuracy
model-index:
  - name: pythia-1b-tulu-v2-mix-nos-rm
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: allenai/ultrafeedback_binarized_cleaned
          type: allenai/ultrafeedback_binarized_cleaned
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7457800511508952

pythia-1b-tulu-v2-mix-nos-rm

This model is a fine-tuned version of kykim0/pythia-1b-tulu-v2-mix-nos on the allenai/ultrafeedback_binarized_cleaned dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5227
  • Accuracy: 0.7458

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: 1.41e-05
  • train_batch_size: 2
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5749 0.0527 100 0.5849 0.6900
0.5873 0.1055 200 0.5581 0.7100
0.5599 0.1582 300 0.5470 0.7212
0.5456 0.2109 400 0.5379 0.7258
0.521 0.2637 500 0.5358 0.7294
0.5361 0.3164 600 0.5363 0.7376
0.5662 0.3691 700 0.5270 0.7412
0.5301 0.4219 800 0.5268 0.7427
0.5661 0.4746 900 0.5301 0.7381
0.5608 0.5274 1000 0.5242 0.7437
0.5223 0.5801 1100 0.5242 0.7422
0.5322 0.6328 1200 0.5249 0.7448
0.4891 0.6856 1300 0.5241 0.7427
0.5111 0.7383 1400 0.5234 0.7437
0.5145 0.7910 1500 0.5225 0.7422
0.4746 0.8438 1600 0.5226 0.7458
0.5551 0.8965 1700 0.5223 0.7448
0.563 0.9492 1800 0.5222 0.7453

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.19.1