llama-2-qlora-ultrachat-200k-processed-indicator-0.6

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the yihanwang617/ultrachat_200k_processed_indicator_0.6_4k dataset. It achieves the following results on the evaluation set:

• Loss: 0.8975

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: 0.0002
• train_batch_size: 2
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• total_eval_batch_size: 16
• 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
0.8864	0.9997	3247	0.8975

Framework versions

• PEFT 0.12.0
• Transformers 4.40.1
• Pytorch 2.4.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1