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/yuchenl4/lmpref/runs/ultrafeedbackSkyworkAgree_alignmentZephyr7BSftFull_sdpo_score_ebs256_lr1e-07_0try1BF7hJnWeg1ItSgrW3LbLmmRFdnX8AvASfq3xa15Ku4Fxb)
+[<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/yuchenl4/lmpref/runs/ultrafeedbackSkyworkAgree_alignmentZephyr7BSftFull_sdpo_score_ebs256_lr1e-07_0try1mH6fAsH07Skaih7aBeXOQGSzIYsG1Ksymn54NyLSigKbX)
 
 This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
 

all_results.json

@@ -1,9 +1,9 @@
 {
     "epoch": 0.9971988795518207,
     "total_flos": 0.0,
-    "train_loss": 0.5609350311622191,
-    "train_runtime": 40948.0878,
+    "train_loss": 0.6403945751404494,
+    "train_runtime": 41762.485,
     "train_samples": 45608,
-    "train_samples_per_second": 1.114,
+    "train_samples_per_second": 1.092,
     "train_steps_per_second": 0.004
 }

train_results.json

@@ -1,9 +1,9 @@
 {
     "epoch": 0.9971988795518207,
     "total_flos": 0.0,
-    "train_loss": 0.5609350311622191,
-    "train_runtime": 40948.0878,
+    "train_loss": 0.6403945751404494,
+    "train_runtime": 41762.485,
     "train_samples": 45608,
-    "train_samples_per_second": 1.114,
+    "train_samples_per_second": 1.092,
     "train_steps_per_second": 0.004
 }