Hugging Face's logo

RefalMachine
/
ruadapt_solar_10.7_darulm_unigram_proj_init_part2_v5

PEFT
Safetensors
Generated from Trainer
Model card Files Community
ruadapt_solar_10.7_darulm_unigram_proj_init_part2_v5 / README.md
RefalMachine's picture
RefalMachine
load
e18453b
preview code
|
raw
history blame contribute delete
7.52 kB
metadata
library_name: peft
tags:
  - generated_from_trainer
metrics:
  - accuracy
base_model: >-
  outputs/solar_10.7_darulm_unigram_proj_init_8node_darulm_part1_v3_1.0_512_12_02_24
model-index:
  - name: >-
      solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v3_1.0_512_28_02_24
    results: []

solar_10.7_darulm_unigram_proj_init_darulm_part2_r128_a512_v3_1.0_512_28_02_24

This model is a fine-tuned version of outputs/solar_10.7_darulm_unigram_proj_init_8node_darulm_part1_v3_1.0_512_12_02_24 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2309
  • Accuracy: 0.5309

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: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 24
  • gradient_accumulation_steps: 5
  • total_train_batch_size: 120
  • total_eval_batch_size: 24
  • optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
  • lr_scheduler_type: linear
  • num_epochs: 1.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.0 1 2.3534 0.5148
2.4427 0.01 500 2.3338 0.5155
2.4399 0.02 1000 2.3276 0.5164
2.4244 0.03 1500 2.3231 0.5169
2.4336 0.04 2000 2.3194 0.5177
2.4201 0.06 2500 2.3156 0.5180
2.4245 0.07 3000 2.3128 0.5185
2.4157 0.08 3500 2.3097 0.5187
2.4054 0.09 4000 2.3070 0.5194
2.4161 0.1 4500 2.3033 0.5197
2.395 0.11 5000 2.3020 0.5201
2.4037 0.12 5500 2.3001 0.5204
2.4188 0.13 6000 2.2977 0.5206
2.406 0.15 6500 2.2961 0.5208
2.4022 0.16 7000 2.2943 0.5210
2.3952 0.17 7500 2.2926 0.5217
2.394 0.18 8000 2.2909 0.5217
2.3828 0.19 8500 2.2891 0.5218
2.3903 0.2 9000 2.2882 0.5223
2.3943 0.21 9500 2.2861 0.5224
2.3944 0.22 10000 2.2851 0.5224
2.3872 0.23 10500 2.2841 0.5227
2.381 0.25 11000 2.2820 0.5228
2.3832 0.26 11500 2.2798 0.5232
2.3813 0.27 12000 2.2793 0.5237
2.3715 0.28 12500 2.2779 0.5241
2.3898 0.29 13000 2.2764 0.5240
2.3717 0.3 13500 2.2757 0.5240
2.3745 0.31 14000 2.2742 0.5244
2.3657 0.32 14500 2.2732 0.5244
2.3782 0.34 15000 2.2715 0.5247
2.3761 0.35 15500 2.2706 0.5247
2.3827 0.36 16000 2.2692 0.5249
2.3659 0.37 16500 2.2678 0.5251
2.3551 0.38 17000 2.2674 0.5252
2.3605 0.39 17500 2.2662 0.5255
2.3579 0.4 18000 2.2654 0.5256
2.361 0.41 18500 2.2642 0.5257
2.3632 0.42 19000 2.2652 0.5254
2.3409 0.44 19500 2.2625 0.5261
2.3546 0.45 20000 2.2631 0.5259
2.361 0.46 20500 2.2611 0.5264
2.355 0.47 21000 2.2598 0.5264
2.3599 0.48 21500 2.2588 0.5265
2.3554 0.49 22000 2.2583 0.5265
2.3552 0.5 22500 2.2571 0.5268
2.3574 0.51 23000 2.2565 0.5268
2.3527 0.53 23500 2.2557 0.5272
2.3574 0.54 24000 2.2548 0.5272
2.3395 0.55 24500 2.2534 0.5274
2.3517 0.56 25000 2.2531 0.5272
2.346 0.57 25500 2.2521 0.5275
2.3469 0.58 26000 2.2515 0.5275
2.3451 0.59 26500 2.2509 0.5278
2.3373 0.6 27000 2.2501 0.5277
2.3512 0.61 27500 2.2493 0.5281
2.3351 0.63 28000 2.2485 0.5282
2.3431 0.64 28500 2.2476 0.5282
2.3399 0.65 29000 2.2463 0.5283
2.3376 0.66 29500 2.2463 0.5284
2.3574 0.67 30000 2.2456 0.5285
2.3312 0.68 30500 2.2447 0.5289
2.3442 0.69 31000 2.2442 0.5288
2.338 0.7 31500 2.2434 0.5289
2.3345 0.72 32000 2.2433 0.5291
2.3314 0.73 32500 2.2420 0.5292
2.326 0.74 33000 2.2414 0.5293
2.3247 0.75 33500 2.2409 0.5295
2.3363 0.76 34000 2.2403 0.5296
2.3409 0.77 34500 2.2395 0.5297
2.335 0.78 35000 2.2391 0.5295
2.3194 0.79 35500 2.2383 0.5298
2.3367 0.8 36000 2.2379 0.5301
2.3286 0.82 36500 2.2372 0.5301
2.3225 0.83 37000 2.2366 0.5302
2.3198 0.84 37500 2.2363 0.5301
2.3274 0.85 38000 2.2355 0.5301
2.3195 0.86 38500 2.2349 0.5303
2.3418 0.87 39000 2.2344 0.5303
2.323 0.88 39500 2.2340 0.5304
2.3211 0.89 40000 2.2336 0.5304
2.3332 0.91 40500 2.2334 0.5306
2.3226 0.92 41000 2.2329 0.5307
2.3329 0.93 41500 2.2325 0.5308
2.3172 0.94 42000 2.2321 0.5307
2.3231 0.95 42500 2.2319 0.5308
2.314 0.96 43000 2.2316 0.5309
2.3205 0.97 43500 2.2315 0.5308
2.3208 0.98 44000 2.2312 0.5309
2.3228 0.99 44500 2.2310 0.5309

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.2
  • Datasets 2.16.1
  • Tokenizers 0.15.2

Training procedure

Framework versions

  • PEFT 0.6.0