llama-3.2-350M-fourier

This model is a fine-tuned version of llama_small_config.json on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.5784

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: 0.0005
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• total_eval_batch_size: 4
• 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_steps: 1000
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
4.6741	0.0552	1000	4.6001
3.6587	0.1105	2000	3.7151
3.2297	0.1657	3000	3.2263
3.0288	0.2210	4000	3.0349
2.9584	0.2762	5000	2.9393
2.9657	0.3315	6000	2.9893
2.8654	0.3867	7000	2.8074
2.6982	0.4420	8000	2.7580
2.7292	0.4972	9000	2.7214
2.7568	0.5525	10000	2.6956
2.6141	0.6077	11000	2.6669
2.631	0.6630	12000	2.6421
2.6837	0.7182	13000	2.6185
2.6257	0.7734	14000	2.6032
2.5669	0.8287	15000	2.5918
2.6383	0.8839	16000	2.5836
2.5749	0.9392	17000	2.5796
2.613	0.9944	18000	2.5784

Framework versions

• Transformers 4.48.2
• Pytorch 2.3.1+cu118
• Datasets 3.2.0
• Tokenizers 0.21.0