barc-llama3.2-1b-instruct-lora64-testtime-finetuning

This model is a fine-tuned version of barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4 on the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems, the barc0/transduction_augmented_test_timearc_all_evaluation_new_seperate and the barc0/transduction_rearc_dataset_400k datasets. It achieves the following results on the evaluation set:

• Loss: 0.0591

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: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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
0.074	1.0	1355	0.0688
0.045	2.0	2710	0.0575
0.0207	3.0	4065	0.0591

Framework versions

• PEFT 0.12.0
• Transformers 4.45.0.dev0
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1