Llama-4-Scout-Legal-Assistant-8bit-QLoRA
This model is a fine-tuned version of meta-llama/Llama-4-Scout-17B-16E-Instruct on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.54.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.0
- Tokenizers 0.21.2
- Downloads last month
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Model tree for QuantumSkynet/Llama-4-Scout-Legal-Assistant-8bit-QLoRA
Base model
meta-llama/Llama-4-Scout-17B-16E