Update README.md

README.md

@@ -184,6 +184,20 @@ model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quan
 model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
 model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "q4_k_m", token = "hf_...")
 
+Loss for 5 epochs in the last training session of the last part of the dataset:
+==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
+   \\   /|    Num examples = 407 | Num Epochs = 5
+O^O/ \_/ \    Batch size per device = 2 | Gradient Accumulation steps = 256
+\        /    Total batch size = 512 | Total steps = 5
+ "-____-"     Number of trainable parameters = 201,850,880
+ [5/5 29:36, Epoch 3/5]
+Step	Training Loss
+1	0.568000
+2	0.145300
+3	0.506100
+4	0.331900
+5	0.276100
+
 Parameters: 
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.