metadata

library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
  - alignment-handbook
  - generated_from_trainer
datasets:
  - barc0/transduction_rearc
model-index:
  - name: barc-llama3.1-8b-instruct-fft-sft-transduction_rearc_lr4e-5_epoch2
    results: []

barc-llama3.1-8b-instruct-fft-sft-transduction_rearc_lr4e-5_epoch2

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

Loss: 0.2794

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: 4e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
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: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.3971	0.9981	259	0.4259
0.2353	1.9961	518	0.2794

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.19.1