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---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_wsc_1745950303
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_wsc_1745950303
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the wsc dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5956
- Num Input Tokens Seen: 14002704
## 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: 2
- eval_batch_size: 2
- seed: 123
- gradient_accumulation_steps: 2
- total_train_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
- training_steps: 40000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:--------:|:-----:|:---------------:|:-----------------:|
| 0.9367 | 1.6024 | 200 | 0.6859 | 70144 |
| 0.7729 | 3.2008 | 400 | 0.6358 | 140304 |
| 0.6178 | 4.8032 | 600 | 0.6251 | 210240 |
| 0.7354 | 6.4016 | 800 | 0.6166 | 279952 |
| 0.8205 | 8.0 | 1000 | 0.6166 | 350224 |
| 0.9947 | 9.6024 | 1200 | 0.6063 | 420256 |
| 0.8109 | 11.2008 | 1400 | 0.6140 | 490496 |
| 0.6329 | 12.8032 | 1600 | 0.6054 | 560224 |
| 0.6829 | 14.4016 | 1800 | 0.6053 | 630560 |
| 0.6086 | 16.0 | 2000 | 0.6093 | 699648 |
| 0.485 | 17.6024 | 2200 | 0.6015 | 769232 |
| 0.9604 | 19.2008 | 2400 | 0.6084 | 839344 |
| 0.6535 | 20.8032 | 2600 | 0.6110 | 909744 |
| 0.6409 | 22.4016 | 2800 | 0.6115 | 979312 |
| 0.7109 | 24.0 | 3000 | 0.6066 | 1049184 |
| 0.7251 | 25.6024 | 3200 | 0.6034 | 1119552 |
| 0.6356 | 27.2008 | 3400 | 0.6066 | 1189008 |
| 0.8557 | 28.8032 | 3600 | 0.6137 | 1259168 |
| 0.759 | 30.4016 | 3800 | 0.6130 | 1329056 |
| 0.9193 | 32.0 | 4000 | 0.6128 | 1399280 |
| 0.7954 | 33.6024 | 4200 | 0.6092 | 1469920 |
| 0.7279 | 35.2008 | 4400 | 0.6051 | 1539184 |
| 0.941 | 36.8032 | 4600 | 0.6034 | 1609648 |
| 0.9295 | 38.4016 | 4800 | 0.6008 | 1679792 |
| 0.7476 | 40.0 | 5000 | 0.6098 | 1749008 |
| 0.8862 | 41.6024 | 5200 | 0.6106 | 1818832 |
| 0.7252 | 43.2008 | 5400 | 0.6087 | 1889136 |
| 0.501 | 44.8032 | 5600 | 0.6182 | 1959008 |
| 0.4602 | 46.4016 | 5800 | 0.6046 | 2028320 |
| 0.7075 | 48.0 | 6000 | 0.6129 | 2098928 |
| 0.7795 | 49.6024 | 6200 | 0.6080 | 2168688 |
| 0.6954 | 51.2008 | 6400 | 0.6075 | 2238752 |
| 0.905 | 52.8032 | 6600 | 0.6000 | 2308816 |
| 0.8237 | 54.4016 | 6800 | 0.6067 | 2379328 |
| 0.6337 | 56.0 | 7000 | 0.6052 | 2448704 |
| 0.8776 | 57.6024 | 7200 | 0.6037 | 2519008 |
| 0.7921 | 59.2008 | 7400 | 0.6066 | 2588608 |
| 0.8712 | 60.8032 | 7600 | 0.6045 | 2659072 |
| 0.6104 | 62.4016 | 7800 | 0.6041 | 2728480 |
| 0.9738 | 64.0 | 8000 | 0.6079 | 2798720 |
| 0.6123 | 65.6024 | 8200 | 0.6013 | 2868672 |
| 0.5486 | 67.2008 | 8400 | 0.6026 | 2939312 |
| 0.4234 | 68.8032 | 8600 | 0.6083 | 3009568 |
| 0.706 | 70.4016 | 8800 | 0.6032 | 3079584 |
| 0.5217 | 72.0 | 9000 | 0.6046 | 3149680 |
| 0.4153 | 73.6024 | 9200 | 0.6172 | 3219680 |
| 0.4354 | 75.2008 | 9400 | 0.6041 | 3289472 |
| 0.6993 | 76.8032 | 9600 | 0.5956 | 3359520 |
| 0.7275 | 78.4016 | 9800 | 0.6037 | 3429568 |
| 0.5396 | 80.0 | 10000 | 0.6079 | 3499648 |
| 0.7598 | 81.6024 | 10200 | 0.6038 | 3569504 |
| 0.7379 | 83.2008 | 10400 | 0.6109 | 3639920 |
| 0.9387 | 84.8032 | 10600 | 0.6056 | 3709520 |
| 0.7098 | 86.4016 | 10800 | 0.5983 | 3779456 |
| 0.6795 | 88.0 | 11000 | 0.6039 | 3849744 |
| 0.7353 | 89.6024 | 11200 | 0.6032 | 3919984 |
| 0.6685 | 91.2008 | 11400 | 0.6080 | 3989872 |
| 0.7216 | 92.8032 | 11600 | 0.6073 | 4059568 |
| 0.8336 | 94.4016 | 11800 | 0.6013 | 4129664 |
| 0.548 | 96.0 | 12000 | 0.6024 | 4199936 |
| 0.9363 | 97.6024 | 12200 | 0.5981 | 4269952 |
| 0.6282 | 99.2008 | 12400 | 0.6110 | 4339040 |
| 0.7682 | 100.8032 | 12600 | 0.6031 | 4409680 |
| 0.9204 | 102.4016 | 12800 | 0.6103 | 4479120 |
| 0.6169 | 104.0 | 13000 | 0.6119 | 4548896 |
| 0.7145 | 105.6024 | 13200 | 0.6044 | 4619216 |
| 0.7454 | 107.2008 | 13400 | 0.6099 | 4689424 |
| 0.7114 | 108.8032 | 13600 | 0.6078 | 4759232 |
| 0.7552 | 110.4016 | 13800 | 0.6081 | 4829120 |
| 0.5361 | 112.0 | 14000 | 0.6138 | 4899024 |
| 0.6323 | 113.6024 | 14200 | 0.5998 | 4968944 |
| 0.7257 | 115.2008 | 14400 | 0.6055 | 5039152 |
| 0.5306 | 116.8032 | 14600 | 0.6010 | 5109312 |
| 0.8061 | 118.4016 | 14800 | 0.6115 | 5179296 |
| 0.7583 | 120.0 | 15000 | 0.6079 | 5249504 |
| 0.818 | 121.6024 | 15200 | 0.6016 | 5319424 |
| 0.909 | 123.2008 | 15400 | 0.6039 | 5389488 |
| 0.9621 | 124.8032 | 15600 | 0.6032 | 5459776 |
| 0.3719 | 126.4016 | 15800 | 0.6107 | 5529760 |
| 0.8277 | 128.0 | 16000 | 0.6074 | 5599968 |
| 0.5884 | 129.6024 | 16200 | 0.6056 | 5671056 |
| 0.6286 | 131.2008 | 16400 | 0.6104 | 5740000 |
| 0.6262 | 132.8032 | 16600 | 0.6098 | 5810288 |
| 0.6929 | 134.4016 | 16800 | 0.6065 | 5880176 |
| 0.6835 | 136.0 | 17000 | 0.6080 | 5950048 |
| 0.7025 | 137.6024 | 17200 | 0.6135 | 6020016 |
| 0.8546 | 139.2008 | 17400 | 0.6162 | 6090672 |
| 0.5158 | 140.8032 | 17600 | 0.6072 | 6160288 |
| 0.7597 | 142.4016 | 17800 | 0.6078 | 6230656 |
| 0.8127 | 144.0 | 18000 | 0.6005 | 6299968 |
| 0.669 | 145.6024 | 18200 | 0.6080 | 6370512 |
| 0.7968 | 147.2008 | 18400 | 0.6064 | 6440784 |
| 0.5663 | 148.8032 | 18600 | 0.6056 | 6510560 |
| 0.6785 | 150.4016 | 18800 | 0.6010 | 6579872 |
| 0.8551 | 152.0 | 19000 | 0.6024 | 6650112 |
| 0.7856 | 153.6024 | 19200 | 0.5996 | 6720368 |
| 0.5416 | 155.2008 | 19400 | 0.6072 | 6790512 |
| 0.7651 | 156.8032 | 19600 | 0.6056 | 6860880 |
| 0.6543 | 158.4016 | 19800 | 0.6175 | 6930576 |
| 0.5508 | 160.0 | 20000 | 0.6053 | 7000640 |
| 0.6528 | 161.6024 | 20200 | 0.6023 | 7070272 |
| 0.6598 | 163.2008 | 20400 | 0.5996 | 7140336 |
| 0.5761 | 164.8032 | 20600 | 0.6078 | 7210816 |
| 0.653 | 166.4016 | 20800 | 0.6016 | 7281392 |
| 0.8061 | 168.0 | 21000 | 0.6057 | 7350960 |
| 0.7621 | 169.6024 | 21200 | 0.6053 | 7421312 |
| 0.6579 | 171.2008 | 21400 | 0.6047 | 7491200 |
| 0.5762 | 172.8032 | 21600 | 0.6003 | 7560976 |
| 0.9284 | 174.4016 | 21800 | 0.6020 | 7631024 |
| 0.6199 | 176.0 | 22000 | 0.6054 | 7700784 |
| 0.7859 | 177.6024 | 22200 | 0.6110 | 7770752 |
| 0.3245 | 179.2008 | 22400 | 0.6039 | 7840832 |
| 0.7359 | 180.8032 | 22600 | 0.6061 | 7911072 |
| 0.7983 | 182.4016 | 22800 | 0.6075 | 7981312 |
| 0.6592 | 184.0 | 23000 | 0.6066 | 8050976 |
| 0.6686 | 185.6024 | 23200 | 0.6060 | 8121312 |
| 0.5448 | 187.2008 | 23400 | 0.6047 | 8191520 |
| 0.5868 | 188.8032 | 23600 | 0.6013 | 8261456 |
| 0.7454 | 190.4016 | 23800 | 0.6131 | 8331664 |
| 1.137 | 192.0 | 24000 | 0.6159 | 8401328 |
| 0.5008 | 193.6024 | 24200 | 0.6039 | 8471232 |
| 0.8048 | 195.2008 | 24400 | 0.6079 | 8540976 |
| 0.6897 | 196.8032 | 24600 | 0.6059 | 8611296 |
| 0.5966 | 198.4016 | 24800 | 0.6075 | 8681264 |
| 0.434 | 200.0 | 25000 | 0.6160 | 8751280 |
| 0.4255 | 201.6024 | 25200 | 0.6050 | 8822192 |
| 0.5553 | 203.2008 | 25400 | 0.6063 | 8891648 |
| 0.6894 | 204.8032 | 25600 | 0.6118 | 8961760 |
| 0.5924 | 206.4016 | 25800 | 0.6104 | 9031568 |
| 0.4732 | 208.0 | 26000 | 0.6030 | 9101088 |
| 0.7517 | 209.6024 | 26200 | 0.6052 | 9171168 |
| 0.3247 | 211.2008 | 26400 | 0.6049 | 9240752 |
| 0.5487 | 212.8032 | 26600 | 0.6017 | 9310960 |
| 0.7838 | 214.4016 | 26800 | 0.6027 | 9380560 |
| 1.0043 | 216.0 | 27000 | 0.6075 | 9450912 |
| 0.4924 | 217.6024 | 27200 | 0.6063 | 9520832 |
| 0.5188 | 219.2008 | 27400 | 0.6075 | 9590800 |
| 0.826 | 220.8032 | 27600 | 0.6111 | 9661456 |
| 0.9029 | 222.4016 | 27800 | 0.6089 | 9731376 |
| 0.5354 | 224.0 | 28000 | 0.6084 | 9801040 |
| 0.6485 | 225.6024 | 28200 | 0.6080 | 9870784 |
| 0.8221 | 227.2008 | 28400 | 0.6132 | 9941408 |
| 0.7324 | 228.8032 | 28600 | 0.6031 | 10011264 |
| 0.7633 | 230.4016 | 28800 | 0.6112 | 10080704 |
| 0.9061 | 232.0 | 29000 | 0.6090 | 10150880 |
| 0.855 | 233.6024 | 29200 | 0.6018 | 10221616 |
| 0.9609 | 235.2008 | 29400 | 0.6006 | 10291664 |
| 0.7309 | 236.8032 | 29600 | 0.6120 | 10361728 |
| 0.7132 | 238.4016 | 29800 | 0.6046 | 10431088 |
| 0.5857 | 240.0 | 30000 | 0.6083 | 10501088 |
| 0.6568 | 241.6024 | 30200 | 0.6097 | 10571488 |
| 0.8502 | 243.2008 | 30400 | 0.6069 | 10640848 |
| 0.7067 | 244.8032 | 30600 | 0.6096 | 10711136 |
| 0.5737 | 246.4016 | 30800 | 0.6039 | 10781136 |
| 0.411 | 248.0 | 31000 | 0.5998 | 10851312 |
| 0.3786 | 249.6024 | 31200 | 0.6112 | 10921664 |
| 0.8119 | 251.2008 | 31400 | 0.6060 | 10991936 |
| 0.7882 | 252.8032 | 31600 | 0.6012 | 11061680 |
| 0.7779 | 254.4016 | 31800 | 0.6105 | 11131872 |
| 0.5879 | 256.0 | 32000 | 0.6011 | 11201520 |
| 0.4562 | 257.6024 | 32200 | 0.6092 | 11271952 |
| 0.8154 | 259.2008 | 32400 | 0.5993 | 11340976 |
| 0.8513 | 260.8032 | 32600 | 0.6082 | 11411056 |
| 0.5301 | 262.4016 | 32800 | 0.5973 | 11481152 |
| 0.4274 | 264.0 | 33000 | 0.6082 | 11550752 |
| 0.7707 | 265.6024 | 33200 | 0.6110 | 11620752 |
| 0.5863 | 267.2008 | 33400 | 0.6022 | 11690464 |
| 0.6638 | 268.8032 | 33600 | 0.6062 | 11761360 |
| 0.8022 | 270.4016 | 33800 | 0.6082 | 11831152 |
| 0.4962 | 272.0 | 34000 | 0.6052 | 11900768 |
| 0.7421 | 273.6024 | 34200 | 0.6155 | 11971616 |
| 0.8621 | 275.2008 | 34400 | 0.6042 | 12041104 |
| 0.4739 | 276.8032 | 34600 | 0.6042 | 12111712 |
| 0.661 | 278.4016 | 34800 | 0.6115 | 12181328 |
| 0.5588 | 280.0 | 35000 | 0.6040 | 12251088 |
| 0.8743 | 281.6024 | 35200 | 0.6042 | 12321616 |
| 0.5744 | 283.2008 | 35400 | 0.6042 | 12391184 |
| 0.6344 | 284.8032 | 35600 | 0.6042 | 12461088 |
| 0.7548 | 286.4016 | 35800 | 0.6042 | 12531520 |
| 1.0844 | 288.0 | 36000 | 0.6042 | 12600944 |
| 0.3644 | 289.6024 | 36200 | 0.6042 | 12670544 |
| 0.7256 | 291.2008 | 36400 | 0.6042 | 12741216 |
| 0.8211 | 292.8032 | 36600 | 0.6042 | 12811584 |
| 0.6064 | 294.4016 | 36800 | 0.6042 | 12881104 |
| 0.5569 | 296.0 | 37000 | 0.6042 | 12951648 |
| 0.5618 | 297.6024 | 37200 | 0.6042 | 13021600 |
| 0.6211 | 299.2008 | 37400 | 0.6042 | 13091888 |
| 0.5256 | 300.8032 | 37600 | 0.6042 | 13162128 |
| 1.1123 | 302.4016 | 37800 | 0.6042 | 13231552 |
| 0.7682 | 304.0 | 38000 | 0.6042 | 13302080 |
| 0.6204 | 305.6024 | 38200 | 0.6042 | 13371808 |
| 0.8488 | 307.2008 | 38400 | 0.6042 | 13441936 |
| 0.947 | 308.8032 | 38600 | 0.6042 | 13512304 |
| 0.8 | 310.4016 | 38800 | 0.6042 | 13582192 |
| 0.802 | 312.0 | 39000 | 0.6042 | 13652384 |
| 0.457 | 313.6024 | 39200 | 0.6042 | 13722224 |
| 0.6368 | 315.2008 | 39400 | 0.6042 | 13791728 |
| 0.5913 | 316.8032 | 39600 | 0.6042 | 13862560 |
| 0.6218 | 318.4016 | 39800 | 0.6042 | 13933264 |
| 0.6923 | 320.0 | 40000 | 0.6042 | 14002704 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |