2.8 KiB
2.8 KiB
library_name, base_model, tags, datasets, model-index
| library_name | base_model | tags | datasets | model-index | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| transformers | W-61/llama-3-8b-base-sft-hh-helpful-8xh200 |
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llama-3-8b-base-margin-dpo-hh-helpful-8xh200-20260410-172009
This model is a fine-tuned version of W-61/llama-3-8b-base-sft-hh-helpful-8xh200 on the Anthropic/hh-rlhf dataset. It achieves the following results on the evaluation set:
- Loss: 0.4588
- Margin Dpo/margin Mean: 11.1187
- Margin Dpo/margin Std: 15.0696
- Logps/chosen: -119.7147
- Logps/rejected: -113.9535
- Logps/ref Chosen: -97.0617
- Logps/ref Rejected: -80.1818
- Logits/chosen: -0.5876
- Logits/rejected: -0.5495
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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- 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 | Margin Dpo/margin Mean | Margin Dpo/margin Std | Logps/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4265 | 0.2941 | 100 | 0.5427 | 5.3418 | 10.0613 | -107.1989 | -95.6607 | -97.0617 | -80.1818 | -0.6361 | -0.6086 |
| 0.3411 | 0.5882 | 200 | 0.4754 | 10.2996 | 14.6526 | -119.3164 | -112.7360 | -97.0617 | -80.1818 | -0.6021 | -0.5640 |
| 0.3552 | 0.8824 | 300 | 0.4588 | 11.1187 | 15.0696 | -119.7147 | -113.9535 | -97.0617 | -80.1818 | -0.5876 | -0.5495 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4