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Model: W-61/llama-3-8b-base-epsilon-dpo-ultrafeedback-8xh200
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: W-61/llama-3-8b-base-sft-ultrachat-8xh200
+tags:
+- alignment-handbook
+- epsilon-dpo
+- generated_from_trainer
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+model-index:
+- name: llama-3-8b-base-epsilon-dpo-ultrafeedback-8xh200-20260411-020915
+  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-3-8b-base-epsilon-dpo-ultrafeedback-8xh200-20260411-020915
+
+This model is a fine-tuned version of [W-61/llama-3-8b-base-sft-ultrachat-8xh200](https://huggingface.co/W-61/llama-3-8b-base-sft-ultrachat-8xh200) on the HuggingFaceH4/ultrafeedback_binarized dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6085
+- Rewards/chosen: -0.6393
+- Rewards/rejected: -0.8881
+- Rewards/accuracies: 0.6905
+- Rewards/margins: 0.2488
+- Logps/chosen: -567.7599
+- Logps/rejected: -657.1562
+- Logps/ref Chosen: -287.9388
+- Logps/ref Rejected: -266.7935
+- Logits/chosen: -0.8106
+- Logits/rejected: -0.7709
+- Kl/p Epsilon Steps: 0.6734
+- Kl/n Epsilon Steps: 0.3185
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- total_eval_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- 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/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected | Kl/p Epsilon Steps | Kl/n Epsilon Steps |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:------------:|:--------------:|:----------------:|:------------------:|:-------------:|:---------------:|:------------------:|:------------------:|
+| 2.3277        | 0.4188 | 200  | 0.5904          | -0.6331        | -0.9468          | 0.7011             | 0.3137          | -411.3474    | -452.2706      | -287.9388        | -266.7935          | -0.8135       | -0.7841         | 0.6885             | 0.3044             |
+| 2.4805        | 0.8377 | 400  | 0.6085          | -0.6393        | -0.8881          | 0.6905             | 0.2488          | -567.7599    | -657.1562      | -287.9388        | -266.7935          | -0.8106       | -0.7709         | 0.6734             | 0.3185             |
+
+
+### Framework versions
+
+- Transformers 4.51.0
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.21.4