Model save

README.md

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+---
+license: llama3.1
+base_model: Magpie-Align/Llama-3.1-8B-Magpie-SFT-650KR
+tags:
+- trl
+- dpo
+- generated_from_trainer
+model-index:
+- name: Llama-3.1-8B-Magpie-SFT-650KR-Magpo-Armorm-3.1-70B-05
+ results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Llama-3.1-8B-Magpie-SFT-650KR-Magpo-Armorm-3.1-70B-05
+
+This model is a fine-tuned version of [Magpie-Align/Llama-3.1-8B-Magpie-SFT-650KR](https://huggingface.co/Magpie-Align/Llama-3.1-8B-Magpie-SFT-650KR) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3335
+- Rewards/chosen: -4.8366
+- Rewards/rejected: -7.5394
+- Rewards/accuracies: 0.8880
+- Rewards/margins: 2.7028
+- Logps/rejected: -1104.0730
+- Logps/chosen: -827.6954
+- Logits/rejected: -0.8119
+- Logits/chosen: -0.8042
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-07
+- train_batch_size: 2
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 128
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.5603 | 0.1306 | 100 | 0.5762 | -1.0828 | -1.5526 | 0.7620 | 0.4698 | -505.3885 | -452.3145 | -0.7241 | -0.7285 |
+| 0.5441 | 0.2612 | 200 | 0.4445 | -3.4116 | -5.1002 | 0.8360 | 1.6886 | -860.1481 | -685.1905 | -0.6966 | -0.6964 |
+| 0.3586 | 0.3919 | 300 | 0.3949 | -3.4100 | -5.2798 | 0.8720 | 1.8698 | -878.1118 | -685.0309 | -0.7677 | -0.7653 |
+| 0.3737 | 0.5225 | 400 | 0.3653 | -4.3580 | -6.6737 | 0.8760 | 2.3157 | -1017.5 | -779.8291 | -0.7777 | -0.7711 |
+| 0.2611 | 0.6531 | 500 | 0.3457 | -4.9017 | -7.6712 | 0.8860 | 2.7695 | -1117.2515 | -834.2015 | -0.8137 | -0.8074 |
+| 0.3342 | 0.7837 | 600 | 0.3354 | -4.7041 | -7.3342 | 0.8920 | 2.6301 | -1083.5503 | -814.4402 | -0.8081 | -0.7999 |
+| 0.3251 | 0.9144 | 700 | 0.3335 | -4.8366 | -7.5394 | 0.8880 | 2.7028 | -1104.0730 | -827.6954 | -0.8119 | -0.8042 |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.4.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

all_results.json

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+{
+ "epoch": 0.9992652461425422,
+ "total_flos": 0.0,
+ "train_loss": 0.41019999388775796,
+ "train_runtime": 41758.0505,
+ "train_samples": 97988,
+ "train_samples_per_second": 2.347,
+ "train_steps_per_second": 0.018
+}

generation_config.json

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+{
+ "_from_model_config": true,
+ "bos_token_id": 128000,
+ "do_sample": true,
+ "eos_token_id": 128001,
+ "temperature": 0.6,
+ "top_p": 0.9,
+ "transformers_version": "4.43.3"
+}

train_results.json

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+{
+ "epoch": 0.9992652461425422,
+ "total_flos": 0.0,
+ "train_loss": 0.41019999388775796,
+ "train_runtime": 41758.0505,
+ "train_samples": 97988,
+ "train_samples_per_second": 2.347,
+ "train_steps_per_second": 0.018
+}