Datasets:
input_ids
int32 0
28.1k
|
|---|
1
|
3,196
|
9,743
|
869
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3,857
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5,632
|
11,312
|
18
|
25
|
0
|
14,453
|
116
|
7
|
634
|
25
|
0
|
11
|
7,768
|
13
|
3,857
|
5,632
|
11,312
|
192
|
180
|
1,616
|
1,767
|
18
|
8
|
11
|
8,223
|
3,276
|
201
|
234
|
3,857
|
5,632
|
11,312
|
14,453
|
116
|
4,591
|
2,857
|
541
|
11
|
263
|
60
|
12,658
|
1,190
|
2,375
|
1,228
|
824
|
1,913
|
250
|
3,197
|
197
|
10,553
|
13
|
13,573
|
226
|
180
|
10,582
|
2,989
|
681
|
13
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25,068
|
130
|
193
|
1,098
|
1,609
|
193
|
541
|
11
|
331
|
263
|
180
|
1,528
|
824
|
193
|
180
|
3,857
|
5,632
|
11,312
|
899
|
13
|
1,662
|
21,691
|
180
|
1,436
|
18,412
|
203
|
192
|
12,658
|
197
|
2,255
|
528
|
5,385
|
234
|
396
|
25,443
|
11
|
180
|
2,153
|
This is the tokenized data of salesforce/wikitext dataset. All the samples in the train set are concatenated for pretraining the llm.
To see how the tokenized dataset is created please see : https://github.com/SSahas/Implementing-LLM-From-Scratch/blob/main/assets/preprocessing.ipynb
PROJECT
Implementing Decoder only Model (GPT style) from scratch with PyTorch
Pretraining a LLM model for Text generation, used Salesforce/wikitext for training. The model was trained for 30000 iterations with a batch size of 8 for ~2.5 hours on Tesla P100 (Kaggle Free gpu support). The training loss is around 3.5. Used adam optimizer with a learning rate of 5e-4. After training, the model is producing little reasonable english, can be trained for more time with bigger n_embd and block size for better generation.
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