Step and Loss

==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
\\ /| Num examples = 967 | Num Epochs = 1
O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 16
\ / Total batch size = 32 | Total steps = 30
"-____-" Number of trainable parameters = 100,925,440
[30/30 26:26, Epoch 0/1]
Step Training Loss
1 1.737000
2 1.738000
3 1.384700
4 1.086400
5 1.009600
6 0.921000
7 0.830400
8 0.808900
9 0.774500
10 0.759900
11 0.736100
12 0.721200
13 0.733200
14 0.701000
15 0.711700
16 0.701400
17 0.689500
18 0.678800
19 0.675200
20 0.680500
21 0.685800
22 0.681200
23 0.672000
24 0.679900
25 0.675500
26 0.666600
27 0.687900
28 0.653600
29 0.672500
30 0.660900

README.md

@@ -14,44 +14,6 @@ tags:
 base_model: unsloth/tinyllama-bnb-4bit
 ---
 
---- >-
-==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num
-examples = 967 | Num Epochs = 1 O^O/ \_/ \ Batch size per device = 2 | Gradient
-Accumulation steps = 16 \ / Total batch size = 32 | Total steps = 30 "-____-"
-Number of trainable parameters = 100,925,440 [30/30 26:26, Epoch 0/1] Step
-Training Loss
-
-1 1.737000 
-2 1.738000 
-3 1.384700 
-4 1.086400 
-5 1.009600 
-6 0.921000 
-7 0.830400 
-8 0.808900 
-9 0.774500 
-10 0.759900 
-11 0.736100 
-12 0.721200 
-13 0.733200 
-14 0.701000
-15 0.711700 
-16 0.701400 
-17 0.689500 
-18 0.678800 
-19 0.675200 
-20 0.680500 
-21 0.685800 
-22 0.681200 
-23 0.672000 
-24 0.679900 
-25 0.675500 
-26 0.666600 
-27 0.687900
-28 0.653600 
-29 0.672500 
-30 0.660900
-
 ---
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