Update README.md

README.md

@@ -14,6 +14,23 @@ 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
+
+---
+null
+
+
 # Uploaded  model
 
 - **Developed by:** Ramikan-BR
@@ -24,43 +41,4 @@ This llama model was trained 2x faster with [Unsloth](https://github.com/unsloth
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
----
-==((====))== 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
----