This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the HuggingFaceH4/deita-10k-v0-sft dataset. It achieves the following results on the evaluation set:

Loss: 0.6346

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.7615	0.92	9	0.8383
0.5005	1.95	19	0.6565
0.365	2.77	27	0.6346

Framework versions

Transformers 4.38.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.2

Minbyul/llama3-8b-instruct-wo-kqa_silver_wogold-iter-sft-step1

llama3-8b-instruct-wo-kqa_silver_wogold-iter-sft-step1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Minbyul/
llama3-8b-instruct-wo-kqa_silver_wogold-iter-sft-step1