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--- |
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license: other |
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base_model: meta-llama/Meta-Llama-3-8B |
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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: C017_random_sample_llama3-8b-base_pretrain_20240504_182259 |
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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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# C017_random_sample_llama3-8b-base_pretrain_20240504_182259 |
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This model is a fine-tuned version of [/data/pro-align/progressalign/shared_storage/downloaded_models/llama3-8b-base](https://huggingface.co//data/pro-align/progressalign/shared_storage/downloaded_models/llama3-8b-base) on the C017_random_sample_data dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4690 |
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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: 1.5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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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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- total_train_batch_size: 64 |
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- total_eval_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: polynomial |
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- lr_scheduler_warmup_steps: 20 |
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- num_epochs: 4.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.5442 | 0.2028 | 200 | 2.5552 | |
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| 2.5376 | 0.4057 | 400 | 2.5096 | |
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| 2.4487 | 0.6085 | 600 | 2.4831 | |
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| 2.5324 | 0.8114 | 800 | 2.4690 | |
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| 2.265 | 1.0142 | 1000 | 2.4733 | |
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| 2.3002 | 1.2170 | 1200 | 2.4736 | |
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| 2.29 | 1.4199 | 1400 | 2.4734 | |
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| 2.2566 | 1.6227 | 1600 | 2.4725 | |
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| 2.3052 | 1.8256 | 1800 | 2.4721 | |
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| 2.2702 | 2.0284 | 2000 | 2.4734 | |
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| 2.2411 | 2.2312 | 2200 | 2.4746 | |
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| 2.2413 | 2.4341 | 2400 | 2.4749 | |
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| 2.216 | 2.6369 | 2600 | 2.4749 | |
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| 2.2696 | 2.8398 | 2800 | 2.4747 | |
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| 2.2455 | 3.0426 | 3000 | 2.4752 | |
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| 2.216 | 3.2454 | 3200 | 2.4753 | |
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| 2.2348 | 3.4483 | 3400 | 2.4757 | |
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| 2.238 | 3.6511 | 3600 | 2.4753 | |
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| 2.2349 | 3.8540 | 3800 | 2.4752 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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