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--- |
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library_name: transformers |
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license: llama3.1 |
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base_model: meta-llama/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: crafting_sft_fail_new_mem |
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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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# crafting_sft_fail_new_mem |
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the identity and the crafting_sft_fail_new_mem datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3208 |
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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: 1 |
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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: 8 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.5341 | 0.0323 | 50 | 0.4793 | |
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| 0.5261 | 0.0646 | 100 | 0.4858 | |
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| 0.5103 | 0.0969 | 150 | 0.4932 | |
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| 0.5236 | 0.1291 | 200 | 0.4820 | |
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| 0.524 | 0.1614 | 250 | 0.4623 | |
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| 0.5107 | 0.1937 | 300 | 0.4454 | |
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| 0.4723 | 0.2260 | 350 | 0.4380 | |
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| 0.4771 | 0.2583 | 400 | 0.4323 | |
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| 0.4835 | 0.2906 | 450 | 0.4249 | |
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| 0.455 | 0.3229 | 500 | 0.4205 | |
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| 0.4724 | 0.3552 | 550 | 0.4145 | |
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| 0.4579 | 0.3874 | 600 | 0.4005 | |
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| 0.4691 | 0.4197 | 650 | 0.4049 | |
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| 0.4405 | 0.4520 | 700 | 0.3883 | |
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| 0.4443 | 0.4843 | 750 | 0.3845 | |
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| 0.4348 | 0.5166 | 800 | 0.3788 | |
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| 0.4153 | 0.5489 | 850 | 0.3675 | |
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| 0.4123 | 0.5812 | 900 | 0.3647 | |
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| 0.3943 | 0.6134 | 950 | 0.3590 | |
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| 0.4059 | 0.6457 | 1000 | 0.3495 | |
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| 0.3778 | 0.6780 | 1050 | 0.3437 | |
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| 0.3734 | 0.7103 | 1100 | 0.3430 | |
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| 0.3762 | 0.7426 | 1150 | 0.3367 | |
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| 0.3576 | 0.7749 | 1200 | 0.3327 | |
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| 0.3794 | 0.8072 | 1250 | 0.3295 | |
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| 0.3695 | 0.8395 | 1300 | 0.3265 | |
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| 0.3571 | 0.8717 | 1350 | 0.3233 | |
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| 0.3655 | 0.9040 | 1400 | 0.3225 | |
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| 0.3801 | 0.9363 | 1450 | 0.3211 | |
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| 0.3704 | 0.9686 | 1500 | 0.3209 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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