# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ubiodee/Cardano_plutus")
model = AutoModelForCausalLM.from_pretrained("ubiodee/Cardano_plutus")Quick Links
Cardano_plutus
This model is a fine-tuned version of meta-llama/Llama-3.2-3B 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for ubiodee/Cardano_plutus
Base model
meta-llama/Llama-3.2-3B
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ubiodee/Cardano_plutus")