library_name, base_model, tags, datasets, model-index
| library_name | base_model | tags | datasets | model-index | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| transformers | W-61/llama-3-8b-base-sft-ultrachat-8xh200 |
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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 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