End of training

README.md

@@ -6,6 +6,7 @@ tags:
 - generated_from_trainer
 datasets:
 - lemon-mint/Korean-FineTome-100k
+- lemon-mint/smol-koreantalk
 model-index:
 - name: ko-tiny-exp
   results: []
@@ -30,6 +31,13 @@ datasets:
     message_property_mappings:
       role: role
       content: content
+  - path: lemon-mint/smol-koreantalk
+    type: chat_template
+    split: train[:20%]
+    field_messages: messages
+    message_property_mappings:
+      role: role
+      content: content
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 
@@ -45,7 +53,6 @@ pad_to_sequence_len: true
 gradient_accumulation_steps: 4
 micro_batch_size: 16
 
-num_epochs: 2
 optimizer: paged_adamw_8bit
 lr_scheduler: cosine
 learning_rate: 2e-5
@@ -61,6 +68,7 @@ logging_steps: 1
 flash_attention: true
 
 warmup_steps: 100
+num_epochs: 2
 evals_per_epoch: 2
 saves_per_epoch: 1
 weight_decay: 0.0
@@ -71,9 +79,9 @@ weight_decay: 0.0
 
 # ko-tiny-exp
 
-This model is a fine-tuned version of [minpeter/pretrained-tiny-ko](https://huggingface.co/minpeter/pretrained-tiny-ko) on the lemon-mint/Korean-FineTome-100k dataset.
+This model is a fine-tuned version of [minpeter/pretrained-tiny-ko](https://huggingface.co/minpeter/pretrained-tiny-ko) on the lemon-mint/Korean-FineTome-100k and the lemon-mint/smol-koreantalk datasets.
 It achieves the following results on the evaluation set:
-- Loss: 2.9471
+- Loss: 2.8226
 
 ## Model description
 
@@ -101,17 +109,16 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_steps: 100
-- training_steps: 176
+- training_steps: 1498
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
-| 3.0538        | 0.0113 | 1    | 3.0136          |
-| 2.9454        | 0.4972 | 44   | 3.0099          |
-| 2.9657        | 0.9944 | 88   | 2.9874          |
-| 3.0426        | 1.4859 | 132  | 2.9506          |
-| 2.9403        | 1.9831 | 176  | 2.9471          |
+| 3.0001        | 0.0013 | 1    | 2.9904          |
+| 2.8288        | 0.5002 | 375  | 2.8669          |
+| 2.8188        | 1.0    | 750  | 2.8255          |
+| 2.8012        | 1.5002 | 1125 | 2.8226          |
 
 
 ### Framework versions