tinyllama_mole_sftv2_ultrachat_ep3

This model is a fine-tuned version of ondevicellm/tinyllama_mole_v1 on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

• Loss: 1.7340

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• 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_steps: 120
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.7643	0.09	100	2.7492
2.7293	0.18	200	2.7330
2.6973	0.26	300	2.6920
2.612	0.35	400	2.6290
2.5257	0.44	500	2.5470
2.4656	0.53	600	2.4527
2.3607	0.61	700	2.3681
2.2885	0.7	800	2.2988
2.2384	0.79	900	2.2397
2.1585	0.88	1000	2.1877
2.1526	0.96	1100	2.1409
2.0845	1.05	1200	2.0986
2.049	1.14	1300	2.0603
2.0243	1.23	1400	2.0257
1.9899	1.31	1500	1.9950
1.9706	1.4	1600	1.9675
1.9414	1.49	1700	1.9429
1.8952	1.58	1800	1.9208
1.9038	1.66	1900	1.9013
1.8942	1.75	2000	1.8839
1.8652	1.84	2100	1.8679
1.823	1.93	2200	1.8531
1.8394	2.01	2300	1.8394
1.8347	2.1	2400	1.8268
1.8137	2.19	2500	1.8148
1.799	2.28	2600	1.8037
1.7774	2.37	2700	1.7931
1.771	2.45	2800	1.7832
1.7761	2.54	2900	1.7739
1.7458	2.63	3000	1.7652
1.7683	2.72	3100	1.7570
1.7389	2.8	3200	1.7490
1.7321	2.89	3300	1.7414
1.7418	2.98	3400	1.7340

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.2+cu118
• Datasets 2.14.6
• Tokenizers 0.15.0