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保留本地 .gitattributes 和 README.md 文件
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- README.md +66 -0
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training_args.bin filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.safetensor filter=lfs diff=lfs merge=lfs -text
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model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md
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<<<<<<< HEAD
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---
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license: mit
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---
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=======
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---
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library_name: transformers
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license: other
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base_model: /online1/ycsc_lijt1/lijt1/wpz/hf_models/Meta-Llama-3.1-8B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: CDG_sl_bsz256
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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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# CDG_sl_bsz256
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This model is a fine-tuned version of [/online1/ycsc_lijt1/lijt1/wpz/hf_models/Meta-Llama-3.1-8B-Instruct](https://huggingface.co//online1/ycsc_lijt1/lijt1/wpz/hf_models/Meta-Llama-3.1-8B-Instruct) on the EI_round1-loop1, the EI_round1-loop2, the mislead-round2-loop2, the correct-round2-loop2, the mislead-round2-loop1 and the correct-round2-loop1 datasets.
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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: 1e-06
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- total_eval_batch_size: 64
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- optimizer: Use 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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- num_epochs: 1.0
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### Training results
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### Framework versions
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- Transformers 4.48.0.dev0
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- Pytorch 2.4.0+cu121
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- Datasets 3.1.0
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- Tokenizers 0.21.0
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>>>>>>> d623a31 (first_commit)
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