qlora-llama70-ft-full-dataset

This model is a fine-tuned version of meta-llama/Llama-2-70b-hf on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.5429

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: float16

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0025
• train_batch_size: 2
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 16
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Framework versions

• PEFT 0.5.0
• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.1
• Tokenizers 0.15.0