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--- |
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library_name: transformers |
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license: llama3.2 |
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base_model: meta-llama/Llama-3.2-1B-Instruct |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gohsyi/metamath-sft |
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metrics: |
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- accuracy |
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model-index: |
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- name: Llama-3.2-1B-Instruct-sft_metamath |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: gohsyi/metamath-sft |
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type: gohsyi/metamath-sft |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8814735253307663 |
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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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# Llama-3.2-1B-Instruct-sft_metamath |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the gohsyi/metamath-sft dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4330 |
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- Accuracy: 0.8815 |
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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: 14 |
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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: 2 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 448 |
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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: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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