Gemma-2b-chat
This model is a fine-tuned version of google/gemma-2b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3272
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.974 | 0.02 | 100 | 2.9419 |
3.1998 | 0.04 | 200 | 2.9117 |
2.9839 | 0.06 | 300 | 2.8745 |
2.9379 | 0.08 | 400 | 2.8328 |
2.9392 | 0.1 | 500 | 2.7909 |
2.7735 | 0.12 | 600 | 2.7461 |
2.7835 | 0.14 | 700 | 2.7075 |
2.7794 | 0.16 | 800 | 2.6695 |
2.7355 | 0.18 | 900 | 2.6363 |
2.7805 | 0.2 | 1000 | 2.6104 |
2.556 | 0.22 | 1100 | 2.5871 |
2.596 | 0.24 | 1200 | 2.5652 |
2.5838 | 0.26 | 1300 | 2.5432 |
2.6598 | 0.28 | 1400 | 2.5247 |
2.5475 | 0.3 | 1500 | 2.5100 |
2.4459 | 0.31 | 1600 | 2.4954 |
2.502 | 0.33 | 1700 | 2.4829 |
2.557 | 0.35 | 1800 | 2.4702 |
2.4944 | 0.37 | 1900 | 2.4604 |
2.4774 | 0.39 | 2000 | 2.4528 |
2.4287 | 0.41 | 2100 | 2.4453 |
2.5386 | 0.43 | 2200 | 2.4381 |
2.363 | 0.45 | 2300 | 2.4322 |
2.514 | 0.47 | 2400 | 2.4272 |
2.413 | 0.49 | 2500 | 2.4225 |
2.4667 | 0.51 | 2600 | 2.4176 |
2.4724 | 0.53 | 2700 | 2.4128 |
2.3949 | 0.55 | 2800 | 2.4084 |
2.4822 | 0.57 | 2900 | 2.4044 |
2.4556 | 0.59 | 3000 | 2.4009 |
2.4067 | 0.61 | 3100 | 2.3977 |
2.3911 | 0.63 | 3200 | 2.3947 |
2.3446 | 0.65 | 3300 | 2.3923 |
2.3358 | 0.67 | 3400 | 2.3891 |
2.3213 | 0.69 | 3500 | 2.3867 |
2.4041 | 0.71 | 3600 | 2.3840 |
2.4759 | 0.73 | 3700 | 2.3818 |
2.4622 | 0.75 | 3800 | 2.3801 |
2.3512 | 0.77 | 3900 | 2.3778 |
2.3653 | 0.79 | 4000 | 2.3760 |
2.3455 | 0.81 | 4100 | 2.3744 |
2.4364 | 0.83 | 4200 | 2.3724 |
2.2805 | 0.85 | 4300 | 2.3706 |
2.5448 | 0.87 | 4400 | 2.3681 |
2.3061 | 0.89 | 4500 | 2.3674 |
2.2572 | 0.9 | 4600 | 2.3657 |
2.3259 | 0.92 | 4700 | 2.3645 |
2.4078 | 0.94 | 4800 | 2.3633 |
2.3841 | 0.96 | 4900 | 2.3618 |
2.5439 | 0.98 | 5000 | 2.3604 |
2.4556 | 1.0 | 5100 | 2.3593 |
2.3752 | 1.02 | 5200 | 2.3582 |
2.3415 | 1.04 | 5300 | 2.3567 |
2.2824 | 1.06 | 5400 | 2.3555 |
2.3748 | 1.08 | 5500 | 2.3541 |
2.2535 | 1.1 | 5600 | 2.3534 |
2.3277 | 1.12 | 5700 | 2.3530 |
2.394 | 1.14 | 5800 | 2.3518 |
2.4876 | 1.16 | 5900 | 2.3511 |
2.4705 | 1.18 | 6000 | 2.3503 |
2.4394 | 1.2 | 6100 | 2.3499 |
2.3898 | 1.22 | 6200 | 2.3488 |
2.3789 | 1.24 | 6300 | 2.3483 |
2.4315 | 1.26 | 6400 | 2.3472 |
2.4065 | 1.28 | 6500 | 2.3463 |
2.3331 | 1.3 | 6600 | 2.3456 |
2.3415 | 1.32 | 6700 | 2.3452 |
2.3433 | 1.34 | 6800 | 2.3448 |
2.337 | 1.36 | 6900 | 2.3434 |
2.4492 | 1.38 | 7000 | 2.3425 |
2.3757 | 1.4 | 7100 | 2.3419 |
2.4124 | 1.42 | 7200 | 2.3412 |
2.2778 | 1.44 | 7300 | 2.3408 |
2.3127 | 1.46 | 7400 | 2.3401 |
2.2558 | 1.48 | 7500 | 2.3398 |
2.4419 | 1.49 | 7600 | 2.3394 |
2.3052 | 1.51 | 7700 | 2.3388 |
2.3212 | 1.53 | 7800 | 2.3387 |
2.3989 | 1.55 | 7900 | 2.3376 |
2.3201 | 1.57 | 8000 | 2.3372 |
2.4111 | 1.59 | 8100 | 2.3364 |
2.3243 | 1.61 | 8200 | 2.3361 |
2.3158 | 1.63 | 8300 | 2.3360 |
2.3065 | 1.65 | 8400 | 2.3357 |
2.3627 | 1.67 | 8500 | 2.3353 |
2.4604 | 1.69 | 8600 | 2.3348 |
2.2451 | 1.71 | 8700 | 2.3346 |
2.3559 | 1.73 | 8800 | 2.3342 |
2.4832 | 1.75 | 8900 | 2.3338 |
2.5064 | 1.77 | 9000 | 2.3335 |
2.2961 | 1.79 | 9100 | 2.3336 |
2.4394 | 1.81 | 9200 | 2.3334 |
2.4337 | 1.83 | 9300 | 2.3332 |
2.2984 | 1.85 | 9400 | 2.3328 |
2.2544 | 1.87 | 9500 | 2.3325 |
2.4421 | 1.89 | 9600 | 2.3321 |
2.2737 | 1.91 | 9700 | 2.3322 |
2.4483 | 1.93 | 9800 | 2.3319 |
2.4371 | 1.95 | 9900 | 2.3314 |
2.3184 | 1.97 | 10000 | 2.3312 |
2.2936 | 1.99 | 10100 | 2.3308 |
2.432 | 2.01 | 10200 | 2.3304 |
2.3306 | 2.03 | 10300 | 2.3301 |
2.3926 | 2.05 | 10400 | 2.3301 |
2.358 | 2.07 | 10500 | 2.3300 |
2.341 | 2.08 | 10600 | 2.3298 |
2.3886 | 2.1 | 10700 | 2.3297 |
2.2559 | 2.12 | 10800 | 2.3296 |
2.4121 | 2.14 | 10900 | 2.3294 |
2.3301 | 2.16 | 11000 | 2.3292 |
2.2807 | 2.18 | 11100 | 2.3290 |
2.3028 | 2.2 | 11200 | 2.3288 |
2.2957 | 2.22 | 11300 | 2.3289 |
2.296 | 2.24 | 11400 | 2.3289 |
2.248 | 2.26 | 11500 | 2.3288 |
2.3639 | 2.28 | 11600 | 2.3286 |
2.4383 | 2.3 | 11700 | 2.3284 |
2.2921 | 2.32 | 11800 | 2.3282 |
2.4594 | 2.34 | 11900 | 2.3282 |
2.4243 | 2.36 | 12000 | 2.3280 |
2.344 | 2.38 | 12100 | 2.3280 |
2.3063 | 2.4 | 12200 | 2.3279 |
2.3875 | 2.42 | 12300 | 2.3280 |
2.3502 | 2.44 | 12400 | 2.3278 |
2.3034 | 2.46 | 12500 | 2.3278 |
2.4234 | 2.48 | 12600 | 2.3277 |
2.2829 | 2.5 | 12700 | 2.3277 |
2.3965 | 2.52 | 12800 | 2.3277 |
2.4046 | 2.54 | 12900 | 2.3274 |
2.3374 | 2.56 | 13000 | 2.3274 |
2.1988 | 2.58 | 13100 | 2.3274 |
2.3893 | 2.6 | 13200 | 2.3274 |
2.3621 | 2.62 | 13300 | 2.3273 |
2.2888 | 2.64 | 13400 | 2.3273 |
2.3928 | 2.66 | 13500 | 2.3273 |
2.3523 | 2.68 | 13600 | 2.3272 |
2.3158 | 2.69 | 13700 | 2.3273 |
2.3453 | 2.71 | 13800 | 2.3273 |
2.3113 | 2.73 | 13900 | 2.3272 |
2.3878 | 2.75 | 14000 | 2.3272 |
2.3361 | 2.77 | 14100 | 2.3273 |
2.2343 | 2.79 | 14200 | 2.3273 |
2.2963 | 2.81 | 14300 | 2.3271 |
2.252 | 2.83 | 14400 | 2.3272 |
2.4307 | 2.85 | 14500 | 2.3272 |
2.2778 | 2.87 | 14600 | 2.3272 |
2.3832 | 2.89 | 14700 | 2.3272 |
2.3611 | 2.91 | 14800 | 2.3272 |
2.3556 | 2.93 | 14900 | 2.3271 |
2.3712 | 2.95 | 15000 | 2.3272 |
2.3667 | 2.97 | 15100 | 2.3272 |
2.3816 | 2.99 | 15200 | 2.3272 |
Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Aditya149/Gemma-2B-chat
Base model
google/gemma-2b