Model save
Browse files- README.md +64 -0
- all_results.json +9 -0
- train_results.json +9 -0
- trainer_state.json +2087 -0
README.md
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| 1 |
+
---
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license: apache-2.0
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base_model: yanolja/EEVE-Korean-Instruct-2.8B-v1.0
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: eeve-2.8B_B
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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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# eeve-2.8B_B
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This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-2.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3918
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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-05
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- train_batch_size: 16
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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: 3
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- total_train_batch_size: 48
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- total_eval_batch_size: 24
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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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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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.2894 | 1.0 | 291 | 0.3918 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 1.0,
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"total_flos": 2.419849045213184e+16,
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"train_loss": 0.4077431839151481,
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"train_runtime": 379.788,
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"train_samples": 13926,
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"train_samples_per_second": 36.668,
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"train_steps_per_second": 0.766
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}
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train_results.json
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{
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"epoch": 1.0,
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"total_flos": 2.419849045213184e+16,
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"train_loss": 0.4077431839151481,
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"train_runtime": 379.788,
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"train_samples": 13926,
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"train_samples_per_second": 36.668,
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"train_steps_per_second": 0.766
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}
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trainer_state.json
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