End of training

README.md

@@ -18,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7798
-- Eval/rewards/chosen: 0.5857
-- Eval/logps/chosen: -186.9679
-- Eval/rewards/rejected: 0.0902
-- Eval/logps/rejected: -228.9668
-- Eval/rewards/margins: 0.4955
-- Eval/kl: 5.4514
+- Loss: 0.8406
+- Eval/rewards/chosen: -0.5872
+- Eval/logps/chosen: -198.6973
+- Eval/rewards/rejected: -0.6580
+- Eval/logps/rejected: -236.4490
+- Eval/rewards/margins: 0.0708
+- Eval/kl: 0.0
 
 ## Model description
 
@@ -43,7 +43,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0001
+- learning_rate: 1e-05
 - train_batch_size: 1
 - eval_batch_size: 2
 - seed: 1234
@@ -58,12 +58,12 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss |        |
 |:-------------:|:-----:|:----:|:---------------:|:------:|
-| 0.4717        | 0.96  | 12   | 0.7882          | 0.0    |
-| 0.6064        | 2.0   | 25   | 0.8020          | 0.0    |
-| 0.2324        | 2.96  | 37   | 0.7275          | 0.0    |
-| 0.4044        | 4.0   | 50   | 0.7863          | 3.8025 |
-| 0.1764        | 4.96  | 62   | 0.7797          | 5.4128 |
-| 0.1937        | 5.76  | 72   | 0.7798          | 5.4514 |
+| 1.7241        | 0.96  | 12   | 0.8748          | 0.0178 |
+| 1.1636        | 2.0   | 25   | 0.8626          | 0.0    |
+| 1.367         | 2.96  | 37   | 0.8516          | 0.0    |
+| 1.0187        | 4.0   | 50   | 0.8411          | 0.0    |
+| 1.1771        | 4.96  | 62   | 0.8434          | 0.0    |
+| 0.9371        | 5.76  | 72   | 0.8406          | 0.0    |
 
 
 ### Framework versions

adapter_model.safetensors

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metrics.jsonl

@@ -40,3 +40,9 @@
 {"epoch": 4.0, "precision": 0.5999999976, "recall": 0.8333333287037037, "fold": 0}
 {"epoch": 4.96, "precision": 0.8571428530612245, "recall": 0.9999999944444444, "fold": 0}
 {"epoch": 5.76, "precision": 0.6521739102079395, "recall": 0.8333333287037037, "fold": 0}
+{"epoch": 0.96, "precision": 0.4736842092797784, "recall": 0.9999999944444444, "fold": 0}
+{"epoch": 2.0, "precision": 0.4615384603550296, "recall": 0.9999999944444444, "fold": 0}
+{"epoch": 2.96, "precision": 0.4999999984375, "recall": 0.8888888839506173, "fold": 0}
+{"epoch": 4.0, "precision": 0.5454545438016529, "recall": 0.9999999944444444, "fold": 0}
+{"epoch": 4.96, "precision": 0.49999999852941174, "recall": 0.9444444391975308, "fold": 0}
+{"epoch": 5.76, "precision": 0.47058823391003457, "recall": 0.8888888839506173, "fold": 0}

metrics_epoch_0.96_fold_0_lr_1e-05_seed_1234_weight_5.0.json

@@ -0,0 +1 @@
+{"epoch": 0.96, "precision": 0.4736842092797784, "recall": 0.9999999944444444, "fold": 0}

metrics_epoch_2.0_fold_0_lr_1e-05_seed_1234_weight_5.0.json

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+{"epoch": 2.0, "precision": 0.4615384603550296, "recall": 0.9999999944444444, "fold": 0}

metrics_epoch_2.96_fold_0_lr_1e-05_seed_1234_weight_5.0.json

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+{"epoch": 2.96, "precision": 0.4999999984375, "recall": 0.8888888839506173, "fold": 0}

metrics_epoch_4.0_fold_0_lr_1e-05_seed_1234_weight_5.0.json

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+{"epoch": 4.0, "precision": 0.5454545438016529, "recall": 0.9999999944444444, "fold": 0}

metrics_epoch_4.96_fold_0_lr_1e-05_seed_1234_weight_5.0.json

@@ -0,0 +1 @@
+{"epoch": 4.96, "precision": 0.49999999852941174, "recall": 0.9444444391975308, "fold": 0}

metrics_epoch_5.76_fold_0_lr_1e-05_seed_1234_weight_5.0.json

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+{"epoch": 5.76, "precision": 0.47058823391003457, "recall": 0.8888888839506173, "fold": 0}

training_args.bin

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