Hugging Face's logo

lewtun
/
Qwen2-0.5B-Reward

Text Classification
Transformers
Safetensors
qwen2
trl
reward-trainer
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
Qwen2-0.5B-Reward / README.md
lewtun's picture
lewtun HF staff
End of training
f52a123 verified
preview code
|
raw
history blame
2.67 kB
metadata
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
  - trl
  - reward-trainer
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Qwen2-0.5B-Reward
    results: []

Qwen2-0.5B-Reward

This model is a fine-tuned version of Qwen/Qwen2-0.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5212
  • Accuracy: 0.731

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-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.6316 0.0516 50 0.5943 0.666
0.573 0.1032 100 0.5857 0.698
0.5809 0.1548 150 0.5718 0.705
0.5493 0.2064 200 0.5450 0.714
0.5649 0.2580 250 0.5483 0.713
0.5585 0.3096 300 0.5265 0.734
0.5431 0.3612 350 0.5295 0.732
0.5209 0.4128 400 0.5334 0.735
0.5414 0.4644 450 0.5409 0.726
0.525 0.5160 500 0.5387 0.731
0.5242 0.5676 550 0.5255 0.727
0.521 0.6192 600 0.5208 0.727
0.5227 0.6708 650 0.5191 0.736
0.5132 0.7224 700 0.5186 0.728
0.5145 0.7740 750 0.5236 0.729
0.514 0.8256 800 0.5249 0.728
0.5087 0.8772 850 0.5261 0.725
0.5009 0.9288 900 0.5229 0.727
0.4989 0.9804 950 0.5213 0.731

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1