This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/induction_100k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 dataset. It achieves the following results on the evaluation set:

Loss: 0.3031

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: 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.323	1.0	758	0.3202
0.2892	2.0	1516	0.3058
0.274	3.0	2274	0.3031

Framework versions

PEFT 0.13.0
Transformers 4.45.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1

barc0/barc-llama3.1-8b-instruct-lora64-induction-gpt4mini100k_lr2e-4_epoch3

barc-llama3.1-8b-instruct-lora64-induction-gpt4mini100k_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-gpt4mini100k_lr2e-4_epoch3