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-slic-hf-ultrafeedback-8xh200
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: 341.9101
- Rewards/chosen: -260.8732
- Rewards/rejected: -246.9854
- Rewards/accuracies: 0.4919
- Rewards/margins: -13.8878
- Logps/chosen: -260.8732
- Logps/rejected: -246.9854
- Slic/rank Loss: 81.0369
- Slic/ce Loss: 260.8732
- Logits/chosen: -0.5998
- Logits/rejected: -0.6056
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 | Slic/rank Loss | Slic/ce Loss | Logits/chosen | Logits/rejected |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1368.2239 | 0.4188 | 200 | 345.5856 | -262.1702 | -246.1536 | 0.4874 | -16.0166 | -262.1702 | -246.1536 | 83.4155 | 262.1702 | -0.6125 | -0.6205 |
| 1395.7896 | 0.8377 | 400 | 341.9101 | -260.8732 | -246.9854 | 0.4919 | -13.8878 | -260.8732 | -246.9854 | 81.0369 | 260.8732 | -0.5998 | -0.6056 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4