arc-heavy-llama3.1-8b-lora64-testtime-finetuning

This model is a fine-tuned version of barc0/Llama-3.1-ARC-Heavy-Transduction-8B on the tttx/test-ttft, the barc0/transduction_formatted_rearc_dataset_100k and the barc0/transduction_heavy_100k_jsonl datasets. It achieves the following results on the evaluation set:

• Loss: 0.0401

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• total_eval_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0373	1.0	667	0.0589
0.0344	2.0	1334	0.0550
0.009	3.0	2001	0.0401

Framework versions

• PEFT 0.13.2
• Transformers 4.46.2
• Pytorch 2.4.0+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3