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--- |
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license: llama2 |
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tags: |
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- llama-factory |
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- legal |
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base_model: sudipto-ducs/InLegalLLaMA |
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model-index: |
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- name: sudipto-ducs/InLegalLLaMA-Instruct |
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results: [] |
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language: |
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- en |
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pipeline_tag: table-question-answering |
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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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# InLegalLLaMA-Instruct |
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This model is a fine-tuned version of [sudipto-ducs/InLegalLLaMA](https://huggingface.co/sudipto-ducs/InLegalLLaMA) on the legalkg_dataset_prompts, the legal_semantic_segmentation and the lima datasets. |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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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: 1000 |
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- num_epochs: 3.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
