51d9aae23d
Model: W-61/qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.5-20260430-194457 Source: Original Platform
63 lines
1.6 KiB
Markdown
63 lines
1.6 KiB
Markdown
---
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library_name: transformers
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base_model: jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128
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tags:
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- alignment-handbook
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- new-dpo
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- generated_from_trainer
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datasets:
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- HuggingFaceH4/ultrafeedback_binarized
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model-index:
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- name: qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.5-20260430-194457
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# qwen3-8b-base-new-dpo-ultrafeedback-4xh200-batch-128-q_t-0.45-s_star-0.5-20260430-194457
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This model is a fine-tuned version of [jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128](https://huggingface.co/jackf857/qwen3-8b-base-sft-ultrachat-4xh200-batch-128) on the HuggingFaceH4/ultrafeedback_binarized dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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- train_batch_size: 4
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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### Framework versions
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- Transformers 4.51.0
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.21.4
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