Model save

README.md

@@ -27,7 +27,7 @@ print(output["generated_text"])
 
 ## Training procedure
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mingzeli/PreThink_MemAgent/runs/cv8dgoef) 
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mingzeli/PreThink_MemAgent/runs/f0f3qo3c) 
 
 
 This model was trained with SFT.

all_results.json

@@ -1,8 +1,8 @@
 {
-    "total_flos": 180679423688704.0,
-    "train_loss": 45.599958295945996,
-    "train_runtime": 3679.7026,
-    "train_samples": 9828,
-    "train_samples_per_second": 5.342,
-    "train_steps_per_second": 0.042
+    "total_flos": 1446350766178304.0,
+    "train_loss": 8.238658274636032,
+    "train_runtime": 17767.8846,
+    "train_samples": 79626,
+    "train_samples_per_second": 8.963,
+    "train_steps_per_second": 0.07
 }

train_results.json

@@ -1,8 +1,8 @@
 {
-    "total_flos": 180679423688704.0,
-    "train_loss": 45.599958295945996,
-    "train_runtime": 3679.7026,
-    "train_samples": 9828,
-    "train_samples_per_second": 5.342,
-    "train_steps_per_second": 0.042
+    "total_flos": 1446350766178304.0,
+    "train_loss": 8.238658274636032,
+    "train_runtime": 17767.8846,
+    "train_samples": 79626,
+    "train_samples_per_second": 8.963,
+    "train_steps_per_second": 0.07
 }