This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/induction_36k_gpt4_description_generated_problems.jsonl_messages_format_0.3 dataset. It achieves the following results on the evaluation set:

Loss: 0.2769

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: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 8
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
0.3222	0.9981	267	0.3000
0.2706	2.0	535	0.2794
0.2563	2.9944	801	0.2769

Framework versions

PEFT 0.13.0
Transformers 4.45.0.dev0
Pytorch 2.1.2
Datasets 3.0.1
Tokenizers 0.19.1

barc0/barc-llama3.1-8b-instruct-lora64-induction-gpt4-mini-36k_lr2e-4_epoch3

barc-llama3.1-8b-instruct-lora64-induction-gpt4-mini-36k_lr2e-4_epoch3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

barc0/
barc-llama3.1-8b-instruct-lora64-induction-gpt4-mini-36k_lr2e-4_epoch3