27acccf3d4
Model: W-61/qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315 Source: Original Platform
library_name, base_model, tags, datasets, model-index
| library_name | base_model | tags | datasets | model-index | |||||||||
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| transformers | W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128 |
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qwen3-8b-base-margin-dpo-ultrafeedback-4xh200-batch-128-20260423-040315
This model is a fine-tuned version of W-61/qwen3-8b-base-sft-ultrachat-4xh200-batch-128 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.5602
- Margin Dpo/margin Mean: 48.7131
- Margin Dpo/margin Std: 68.1546
- Logps/chosen: -316.0414
- Logps/rejected: -345.1451
- Logps/ref Chosen: -281.4589
- Logps/ref Rejected: -261.8495
- Logits/chosen: 1.1933
- Logits/rejected: 1.2367
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 | Margin Dpo/margin Mean | Margin Dpo/margin Std | Logps/chosen | Logps/rejected | Logps/ref Chosen | Logps/ref Rejected | Logits/chosen | Logits/rejected |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4.9383 | 0.4188 | 200 | 0.5970 | 28.2584 | 39.0244 | -287.0227 | -295.6718 | -281.4589 | -261.8495 | 1.4300 | 1.4697 |
| 4.2739 | 0.8377 | 400 | 0.5602 | 48.7131 | 68.1546 | -316.0414 | -345.1451 | -281.4589 | -261.8495 | 1.1933 | 1.2367 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.21.4