788b05c009
Model: jackf857/llama-3-8b-base-cpo-ultrafeedback-4xH200-batch-128-rerun Source: Original Platform
2.6 KiB
2.6 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-ultrachat-8xh200 |
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llama-3-8b-base-cpo-ultrafeedback-4xh200-batch-128
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: 2.0330
- Rewards/chosen: -2.7266
- Rewards/rejected: -2.6680
- Rewards/accuracies: 0.5160
- Rewards/margins: -0.0586
- Logps/rejected: -266.8027
- Logps/chosen: -272.6577
- Logits/rejected: -0.7176
- Logits/chosen: -0.7199
- Nll Loss: 0.9493
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: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- 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 | Nll Loss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17.0014 | 0.4188 | 200 | 2.0831 | -2.7051 | -2.5590 | 0.5020 | -0.1461 | -255.9008 | -270.5104 | -0.6742 | -0.6767 | 0.9401 |
| 16.5359 | 0.8377 | 400 | 2.0330 | -2.7266 | -2.6680 | 0.5160 | -0.0586 | -266.8027 | -272.6577 | -0.7176 | -0.7199 | 0.9493 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4