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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: HuggingFaceTB/SmolLM2-1.7B |
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tags: |
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- alignment-handbook |
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- trl |
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- sft |
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- generated_from_trainer |
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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: HuggingFaceTB/SmolLM2-1.7B |
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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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# HuggingFaceTB/SmolLM2-1.7B |
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This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7823 |
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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-06 |
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- train_batch_size: 8 |
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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: 4 |
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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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- lr_scheduler_warmup_ratio: 0.05 |
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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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| 1.3348 | 0.0551 | 200 | 1.2704 | |
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| 1.0411 | 0.1101 | 400 | 1.0435 | |
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| 1.0483 | 0.1652 | 600 | 0.9694 | |
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| 0.8801 | 0.2202 | 800 | 0.9227 | |
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| 0.8996 | 0.2753 | 1000 | 0.8888 | |
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| 0.8682 | 0.3303 | 1200 | 0.8648 | |
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| 0.8757 | 0.3854 | 1400 | 0.8468 | |
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| 0.8441 | 0.4404 | 1600 | 0.8311 | |
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| 0.8197 | 0.4955 | 1800 | 0.8206 | |
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| 0.7807 | 0.5505 | 2000 | 0.8090 | |
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| 0.7757 | 0.6056 | 2200 | 0.8015 | |
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| 0.7818 | 0.6607 | 2400 | 0.7957 | |
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| 0.8235 | 0.7157 | 2600 | 0.7915 | |
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| 0.7854 | 0.7708 | 2800 | 0.7883 | |
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| 0.7958 | 0.8258 | 3000 | 0.7863 | |
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| 0.8192 | 0.8809 | 3200 | 0.7829 | |
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| 0.765 | 0.9359 | 3400 | 0.7824 | |
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| 0.7939 | 0.9910 | 3600 | 0.7824 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.21.0 |
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