Qwen3-14b-lora-codeAlpaca_20k

This model is a fine-tuned version of Qwen3-14B on codeAlpaca20k dataset.

Model Overview

Qwen3-14b-lora-codeAlpaca_20k has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Trainable parameters: 64,225,280
• Number of Layers: 40
• Number of Attention Heads (GQA): 40 QKV
• Context Length: 32,768

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-4
• train_batch_size: 2
• seed: 3407
• gradient_accumulation_steps:4
• Total_batch_size:8
• optimizer: Adamw_8bit
• lr_scheduler_type: linear
• training_steps: 100

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 2.14.4
• Tokenizers 0.21.1

Citation

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}