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--- |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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library_name: peft |
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license: llama3.1 |
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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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- text-classification |
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model-index: |
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- name: outputs |
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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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# outputs |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) and is intended for text classification tasks. It has been trained to classify text based on the provided labels in the training dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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This model is intended for text classification tasks such as sentiment analysis, spam detection, or other binary/multiclass classification problems. |
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**Limitations**: |
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- The model might not perform well on tasks it has not been explicitly trained for. |
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- The performance may vary depending on the domain and the quality of the input data. |
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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: 0.0001 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 3407 |
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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_steps: 10 |
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- num_epochs: 1 |
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### Training results |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.43.3 |
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- Pytorch 2.4.0+cu124 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
