metadata

license: llama2
library_name: peft
tags:
  - alignment-handbook
  - generated_from_trainer
  - trl
  - sft
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrachat_200k
base_model: lmsys/vicuna-7b-v1.5
model-index:
  - name: vicuna-qlora
    results: []

vicuna-qlora

This model is a fine-tuned version of lmsys/vicuna-7b-v1.5 on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

Loss: 0.9819

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: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 32
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
0.9771	1.0	570	0.9819

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.2.1+cu121
Datasets 2.14.6
Tokenizers 0.15.2