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-ipo-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: 20.1926
- Rewards/chosen: -0.1172
- Rewards/rejected: -0.2002
- Rewards/accuracies: 0.7621
- Rewards/margins: 0.0830
- Logps/rejected: -3.3356
- Logps/chosen: -2.3107
- Logits/rejected: -0.6593
- Logits/chosen: -0.6771
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/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 87.5502 | 0.4188 | 200 | 21.8803 | -0.0519 | -0.1012 | 0.7056 | 0.0493 | -2.3452 | -1.6582 | -0.7170 | -0.7410 |
| 80.9952 | 0.8377 | 400 | 20.1926 | -0.1172 | -0.2002 | 0.7621 | 0.0830 | -3.3356 | -2.3107 | -0.6593 | -0.6771 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4